CN113282657A - Frequent item business data mining analysis method and business data mining equipment - Google Patents
Frequent item business data mining analysis method and business data mining equipment Download PDFInfo
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- CN113282657A CN113282657A CN202110697949.1A CN202110697949A CN113282657A CN 113282657 A CN113282657 A CN 113282657A CN 202110697949 A CN202110697949 A CN 202110697949A CN 113282657 A CN113282657 A CN 113282657A
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Abstract
The frequent item service data mining analysis method and the service data mining equipment provided by the embodiment of the disclosure acquire the initialized service data attribute by performing initialized service data mining analysis on target service data, further perform service data mining analysis according to service data attribute association, service attribute recombination and service data type, and mine and analyze the target frequent item service data in the target service data. Based on the method, the reliability of the business data mining and the business data mining efficiency can be improved by aiming at the mining analysis of the target frequent business data.
Description
Technical Field
The utility model relates to the technical field of big data mining, in particular to a frequent business data mining analysis method and a business data mining device.
Background
With the development of network information technology, massive big data can be generated anytime and anywhere in a network environment. Based on various service providing service platforms, a large amount of service data is generated for various services. However, the existing mining analysis method for business data still has the problems of poor reliability and low efficiency of business data mining.
Disclosure of Invention
In order to at least partially solve the above problem, an embodiment of the present disclosure provides a frequent item service data mining analysis method, where the method includes:
performing initialized service data mining and analysis on target service data to obtain initialized service data attributes, and performing service data attribute association on the initialized service data attributes to obtain first service data attributes;
performing data mining analysis under a first service data type aiming at target frequent item service data according to the first service data attribute to obtain an initialized service data attribute characteristic used for representing the target frequent item service data, performing attribute recombination on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the recombined service data attribute to obtain a second service data attribute;
acquiring a service data weight parameter corresponding to each service data attribute feature in the initialized service data attribute features; performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement;
and performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment.
Further, the performing service data attribute association on the initialized service data attribute to obtain a first service data attribute includes:
performing first frequent item service data mining analysis on the initialized service data attribute through a first service data attribute analysis layer of a frequent item service data mining model to obtain an attribute component of the initialized service data attribute;
and performing service data attribute association on the attribute component through a first attribute association layer of the frequent service data mining model to obtain the first service data attribute.
Further, the performing, according to the first service data attribute, data mining analysis in a first service data type for the target frequent item service data to obtain an initialized service data attribute feature for representing the target frequent item service data includes:
extracting the service data attribute characteristics of the target frequent item service data in the target service data according to the first service data attribute through a first service data characteristic convolution layer of a frequent item service data mining model;
and clustering the service data attribute characteristics of the target frequent item service data to obtain initialized service data attribute characteristics for representing the target frequent item service data.
Further, the attribute restructuring the initialized service data attribute and the first service data attribute, and performing service data attribute association on the restructured service data attribute to obtain a second service data attribute includes:
inputting the initialized service data attribute and the first service data attribute into a second service data attribute analysis layer of a frequent item service data mining model;
adjusting the network parameters of the second service data attribute analysis layer according to the initialized service data attribute and the first service data attribute characteristics to obtain characteristic-adjusted network parameters;
performing attribute recombination on the initialized service data attribute and the first service data attribute to obtain a recombined service data attribute;
performing first frequent item service data mining analysis on the recombined service data attribute according to the network parameter after the characteristic adjustment to obtain an attribute component corresponding to the recombined service data attribute;
and performing service data attribute association on the attribute component through a second attribute association layer of the frequent service data mining model to obtain a second service data attribute.
Further, the sequentially arranging the service data attribute features after the feature filtering according to the feature topological relation of the service data attribute features after the feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the sequentially arranged service data attribute features includes:
acquiring a characteristic topological mapping relation of the characteristic filtered service data attribute characteristics in frequent service data distribution corresponding to the first service data attribute;
and sequentially arranging the service data attribute features after the feature filtering according to the feature topological mapping relation to obtain the service data attribute features after sequential arrangement.
An embodiment of the present disclosure further provides a service data mining device, which includes a processor, a memory, and a frequent item service data mining analysis apparatus, where the frequent item service data mining analysis apparatus includes:
the system comprises an initialization analysis unit, a first service data attribute acquisition unit and a second service data attribute acquisition unit, wherein the initialization analysis unit is used for performing initialization service data mining analysis on target service data to obtain an initialization service data attribute and performing service data attribute association on the initialization service data attribute to obtain a first service data attribute;
the attribute reorganization unit is used for performing data mining analysis under a first service data type aiming at the target frequent item service data according to the first service data attribute to obtain an initialized service data attribute characteristic used for representing the target frequent item service data, performing attribute reorganization on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the reorganized service data attribute to obtain a second service data attribute;
the attribute feature filtering unit is used for acquiring service data weight parameters corresponding to each service data attribute feature in the initialized service data attribute features; performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement;
and the data mining analysis unit is used for performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement so as to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment.
Further, the initialization parsing unit is further configured to:
performing first frequent item service data mining analysis on the initialized service data attribute through a first service data attribute analysis layer of a frequent item service data mining model to obtain an attribute component of the initialized service data attribute;
and performing service data attribute association on the attribute component through a first attribute association layer of the frequent service data mining model to obtain the first service data attribute.
Further, the attribute reorganizing unit is further configured to:
extracting the service data attribute characteristics of the target frequent item service data in the target service data according to the first service data attribute through a first service data characteristic convolution layer of a frequent item service data mining model;
and clustering the service data attribute characteristics of the target frequent item service data to obtain initialized service data attribute characteristics for representing the target frequent item service data.
Further, the attribute reorganizing unit is further configured to:
inputting the initialized service data attribute and the first service data attribute into a second service data attribute analysis layer of a frequent item service data mining model;
adjusting the network parameters of the second service data attribute analysis layer according to the initialized service data attribute and the first service data attribute characteristics to obtain characteristic-adjusted network parameters;
performing attribute recombination on the initialized service data attribute and the first service data attribute to obtain a recombined service data attribute;
performing first frequent item service data mining analysis on the recombined service data attribute according to the network parameter after the characteristic adjustment to obtain an attribute component corresponding to the recombined service data attribute;
and performing service data attribute association on the attribute component through a second attribute association layer of the frequent service data mining model to obtain a second service data attribute.
Further, the attribute feature filtering unit is further configured to:
acquiring a characteristic topological mapping relation of the characteristic filtered service data attribute characteristics in frequent service data distribution corresponding to the first service data attribute;
and sequentially arranging the service data attribute features after the feature filtering according to the feature topological mapping relation to obtain the service data attribute features after sequential arrangement.
In summary, the frequent service data mining analysis method and the service data mining device provided in the embodiments of the present disclosure perform initial service data mining analysis on target service data to obtain an initial service data attribute, and perform service data attribute association on the initial service data attribute to obtain a first service data attribute; then, performing data mining analysis under a first service data type aiming at target frequent item service data according to the first service data attribute to obtain an initialized service data attribute characteristic used for representing the target frequent item service data, performing attribute recombination on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the recombined service data attribute to obtain a second service data attribute; secondly, acquiring a service data weight parameter corresponding to each service data attribute feature in the initialized service data attribute features; performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement; and finally, performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment. Based on the method, the reliability of the business data mining and the business data mining efficiency can be improved by aiming at the mining analysis of the target frequent business data.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings are only some embodiments of the present disclosure, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic diagram of a business data mining device for implementing a frequent business data mining analysis method according to an embodiment of the present disclosure.
Fig. 2 is a schematic flowchart of a frequent business data mining analysis method provided in an embodiment of the present disclosure.
Fig. 3 is a functional unit block diagram of a frequent item service data mining and analyzing apparatus provided in an embodiment of the present disclosure.
Detailed Description
For the purpose of better understanding of the embodiments of the present disclosure by the students in the field of technology, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present disclosure belong to the protection scope of the present disclosure.
Fig. 1 is a schematic diagram of a business data mining device 100 for implementing a frequent item business data mining analysis method according to an embodiment of the present disclosure. Fig. 2 is a schematic flowchart of a frequent business data mining analysis method provided in an embodiment of the present disclosure. In an alternative embodiment, the method is implemented by the business data mining device 100, as exemplified below.
Step S21, performing initial service data mining and parsing on the target service data to obtain an initial service data attribute, and performing service data attribute association on the initial service data attribute to obtain a first service data attribute.
Step S22, performing data mining analysis under the first service data type for the target frequent item service data according to the first service data attribute, to obtain an initialized service data attribute feature for representing the target frequent item service data, performing attribute reorganization on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the reorganized service data attribute, to obtain a second service data attribute.
Step S23, acquiring the service data weight parameter corresponding to each service data attribute feature in the initialized service data attribute features; and performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than the weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement.
Step S24, performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement, so as to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment.
In an alternative embodiment, in step S21, the performing service data attribute association on the initialized service data attribute to obtain a first service data attribute includes:
performing first frequent item service data mining analysis on the initialized service data attribute through a first service data attribute analysis layer of a frequent item service data mining model to obtain an attribute component of the initialized service data attribute;
and performing service data attribute association on the attribute component through a first attribute association layer of the frequent service data mining model to obtain the first service data attribute.
In an alternative embodiment, in step S22, the performing, according to the first service data attribute, data mining analysis in a first service data type for target frequent service data to obtain an initialized service data attribute feature for characterizing the target frequent service data includes:
extracting the service data attribute characteristics of the target frequent item service data in the target service data according to the first service data attribute through a first service data characteristic convolution layer of a frequent item service data mining model;
and clustering the service data attribute characteristics of the target frequent item service data to obtain initialized service data attribute characteristics for representing the target frequent item service data.
In an alternative embodiment, in step S23, the attribute reorganizing the initialized service data attribute and the first service data attribute, and performing service data attribute association on the reorganized service data attribute to obtain a second service data attribute includes:
inputting the initialized service data attribute and the first service data attribute into a second service data attribute analysis layer of a frequent item service data mining model;
adjusting the network parameters of the second service data attribute analysis layer according to the initialized service data attribute and the first service data attribute characteristics to obtain characteristic-adjusted network parameters;
performing attribute recombination on the initialized service data attribute and the first service data attribute to obtain a recombined service data attribute;
performing first frequent item service data mining analysis on the recombined service data attribute according to the network parameter after the characteristic adjustment to obtain an attribute component corresponding to the recombined service data attribute;
and performing service data attribute association on the attribute component through a second attribute association layer of the frequent service data mining model to obtain a second service data attribute.
In an alternative embodiment, in step S23, the sequentially arranging, according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute, the service data attribute features after feature filtering to obtain sequentially arranged service data attribute features includes:
acquiring a characteristic topological mapping relation of the characteristic filtered service data attribute characteristics in frequent service data distribution corresponding to the first service data attribute;
and sequentially arranging the service data attribute features after the feature filtering according to the feature topological mapping relation to obtain the service data attribute features after sequential arrangement.
Further, referring to fig. 2, in this embodiment, the business data mining device 100 may be a server, or may be a server cluster, a computer device, a cloud service center, and other devices with information processing and analyzing capabilities, and the business data mining device 100 may include one or more processors 101, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The business data mining device 100 may also include a machine-readable storage medium 102 for storing any kind of information such as code, settings, data, etc. Non-limiting examples of the machine-readable storage medium include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, machine-readable storage media may store information using any technology. Further, the machine-readable storage medium may provide volatile or non-volatile retention of information. Further, the machine-readable storage medium may represent fixed or removable components of the business data mining device 100. In one case, when the processor 101 executes associated instructions stored in the machine-readable storage medium 102 or a combination of storage media, the business data mining device 100 may perform any of the operations of the associated instructions.
In addition, the business data mining device 100 may also include input/output (I/O) for receiving various inputs (via the input unit) and for providing various outputs (via the output unit)). One particular output mechanism may include a presentation device and an associated Graphical User Interface (GUI). The business data mining device 100 may also include one or more network interfaces for exchanging data with other devices via one or more communication units. One or more communication buses couple the above-described components together.
The communication unit may be implemented in any manner, e.g., over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication units may comprise any combination of hardwired links, wireless links, routers, gateway functions, etc., governed by any protocol or combination of protocols.
Fig. 3 is a functional unit diagram of a frequent business data mining analysis device 103 (shown in fig. 1) provided in an embodiment of the present disclosure, where functions implemented by the frequent business data mining analysis device 103 may correspond to steps executed by the foregoing method. In other embodiments, the frequent service data mining analysis device 103 may be the service data mining device 100, or the processor 101 of the service data mining device, or may be a component that is independent from the service data mining device 100 or the processor 101 and implements the functions of the present disclosure under the control of the service data mining device 100, as shown in fig. 3, and the functions of each functional unit of the frequent service data mining analysis device are explained in detail below.
An initialization analysis unit 1031, configured to perform initialization service data mining analysis on target service data to obtain an initialization service data attribute, and perform service data attribute association on the initialization service data attribute to obtain a first service data attribute;
an attribute reorganizing unit 1032, configured to perform data mining analysis under a first service data type for the target frequent item service data according to the first service data attribute, to obtain an initialized service data attribute feature for representing the target frequent item service data, perform attribute reorganization on the initialized service data attribute and the first service data attribute, perform service data attribute association on the reorganized service data attribute, and obtain a second service data attribute;
an attribute feature filtering unit 1033, configured to obtain a service data weight parameter corresponding to each service data attribute feature in the initialized service data attribute features, perform feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value, to obtain service data attribute features after feature filtering, and sequentially arrange the service data attribute features after feature filtering according to a feature topological relation of the service data attribute features after feature filtering and a feature topological relation corresponding to the first service data attribute, to obtain service data attribute features after sequential arrangement;
a data mining analysis unit 1034, configured to perform data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute features after the sequential arrangement, so as to perform feature adjustment on the service data attribute features after the sequential arrangement, and mine and analyze the target frequent item service data in the target service data according to the service data attribute features after the feature adjustment.
Further, the initialization parsing unit 1031 is further configured to:
performing first frequent item service data mining analysis on the initialized service data attribute through a first service data attribute analysis layer of a frequent item service data mining model to obtain an attribute component of the initialized service data attribute;
and performing service data attribute association on the attribute component through a first attribute association layer of the frequent service data mining model to obtain the first service data attribute.
Further, the attribute reorganizing unit 1032 is further configured to:
extracting the service data attribute characteristics of the target frequent item service data in the target service data according to the first service data attribute through a first service data characteristic convolution layer of a frequent item service data mining model;
and clustering the service data attribute characteristics of the target frequent item service data to obtain initialized service data attribute characteristics for representing the target frequent item service data.
Further, the attribute reorganizing unit 1032 is further configured to:
inputting the initialized service data attribute and the first service data attribute into a second service data attribute analysis layer of a frequent item service data mining model;
adjusting the network parameters of the second service data attribute analysis layer according to the initialized service data attribute and the first service data attribute characteristics to obtain characteristic-adjusted network parameters;
performing attribute recombination on the initialized service data attribute and the first service data attribute to obtain a recombined service data attribute;
performing first frequent item service data mining analysis on the recombined service data attribute according to the network parameter after the characteristic adjustment to obtain an attribute component corresponding to the recombined service data attribute;
and performing service data attribute association on the attribute component through a second attribute association layer of the frequent service data mining model to obtain a second service data attribute.
Further, the attribute feature filtering unit 1033 is further configured to:
acquiring a characteristic topological mapping relation of the characteristic filtered service data attribute characteristics in frequent service data distribution corresponding to the first service data attribute;
and sequentially arranging the service data attribute features after the feature filtering according to the feature topological mapping relation to obtain the service data attribute features after sequential arrangement.
In summary, the frequent service data mining analysis method and the service data mining device provided in the embodiments of the present disclosure perform initial service data mining analysis on target service data to obtain an initial service data attribute, and perform service data attribute association on the initial service data attribute to obtain a first service data attribute; then, performing data mining analysis under a first service data type aiming at target frequent item service data according to the first service data attribute to obtain an initialized service data attribute characteristic used for representing the target frequent item service data, performing attribute recombination on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the recombined service data attribute to obtain a second service data attribute; secondly, acquiring a service data weight parameter corresponding to each service data attribute feature in the initialized service data attribute features; performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement; and finally, performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment. Based on the method, the reliability of the business data mining and the business data mining efficiency can be improved by aiming at the mining analysis of the target frequent business data.
It will be evident to those skilled in the art that the disclosure is not limited to the details of the foregoing illustrative embodiments, and that the present disclosure may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the disclosure being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any drawing credit or debit acknowledgement in the claims should not be construed as limiting the claim concerned.
Claims (8)
1. A frequent business data mining analysis method is characterized by comprising the following steps:
performing initialized service data mining and analysis on target service data to obtain initialized service data attributes, and performing service data attribute association on the initialized service data attributes to obtain first service data attributes;
performing data mining analysis under a first service data type aiming at target frequent item service data according to the first service data attribute to obtain an initialized service data attribute characteristic used for representing the target frequent item service data, performing attribute recombination on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the recombined service data attribute to obtain a second service data attribute;
acquiring a service data weight parameter corresponding to each service data attribute feature in the initialized service data attribute features;
performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement;
and performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment.
2. The method of claim 1, wherein the performing service data attribute association on the initialized service data attribute to obtain a first service data attribute comprises:
performing first frequent item service data mining analysis on the initialized service data attribute through a first service data attribute analysis layer of a frequent item service data mining model to obtain an attribute component of the initialized service data attribute;
performing service data attribute association on the attribute component through a first attribute association layer of the frequent service data mining model to obtain a first service data attribute;
the data mining analysis under the first service data type aiming at the target frequent item service data is carried out according to the first service data attribute to obtain the initialized service data attribute characteristic used for representing the target frequent item service data, and the method comprises the following steps:
extracting the service data attribute characteristics of the target frequent item service data in the target service data according to the first service data attribute through a first service data characteristic convolution layer of a frequent item service data mining model;
and clustering the service data attribute characteristics of the target frequent item service data to obtain initialized service data attribute characteristics for representing the target frequent item service data.
3. The method of claim 2, wherein the performing attribute reassembly for the initialized service data attribute and the first service data attribute, and performing service data attribute association for the reassembled service data attribute to obtain a second service data attribute comprises:
inputting the initialized service data attribute and the first service data attribute into a second service data attribute analysis layer of a frequent item service data mining model;
adjusting the network parameters of the second service data attribute analysis layer according to the initialized service data attribute and the first service data attribute characteristics to obtain characteristic-adjusted network parameters;
performing attribute recombination on the initialized service data attribute and the first service data attribute to obtain a recombined service data attribute;
performing first frequent item service data mining analysis on the recombined service data attribute according to the network parameter after the characteristic adjustment to obtain an attribute component corresponding to the recombined service data attribute;
and performing service data attribute association on the attribute component through a second attribute association layer of the frequent service data mining model to obtain a second service data attribute.
4. The method according to claim 3, wherein the sequentially arranging the service data attribute features after the feature filtering according to the feature topological relation of the service data attribute features after the feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the sequentially arranged service data attribute features comprises:
acquiring a characteristic topological mapping relation of the characteristic filtered service data attribute characteristics in frequent service data distribution corresponding to the first service data attribute;
and sequentially arranging the service data attribute features after the feature filtering according to the feature topological mapping relation to obtain the service data attribute features after sequential arrangement.
5. The business data mining equipment is characterized by comprising a processor, a memory and a frequent business data mining and analyzing device, wherein the frequent business data mining and analyzing device comprises:
the system comprises an initialization analysis unit, a first service data attribute acquisition unit and a second service data attribute acquisition unit, wherein the initialization analysis unit is used for performing initialization service data mining analysis on target service data to obtain an initialization service data attribute and performing service data attribute association on the initialization service data attribute to obtain a first service data attribute;
the attribute reorganization unit is used for performing data mining analysis under a first service data type aiming at the target frequent item service data according to the first service data attribute to obtain an initialized service data attribute characteristic used for representing the target frequent item service data, performing attribute reorganization on the initialized service data attribute and the first service data attribute, and performing service data attribute association on the reorganized service data attribute to obtain a second service data attribute;
the attribute feature filtering unit is used for acquiring service data weight parameters corresponding to each service data attribute feature in the initialized service data attribute features; performing feature filtering on the service data attribute features of which the service data weight parameters are smaller than a weight threshold value to obtain service data attribute features after feature filtering, and sequentially arranging the service data attribute features after feature filtering according to the feature topological relation of the service data attribute features after feature filtering and the feature topological relation corresponding to the first service data attribute to obtain the service data attribute features after sequential arrangement;
and the data mining analysis unit is used for performing data mining analysis under a second service data type on the target frequent item service data according to the second service data attribute and the service data attribute characteristics after the sequential arrangement so as to perform characteristic adjustment on the service data attribute characteristics after the sequential arrangement, and mining and analyzing the target frequent item service data in the target service data according to the service data attribute characteristics after the characteristic adjustment.
6. The traffic data mining device of claim 5, wherein the initialization parsing unit is further configured to:
performing first frequent item service data mining analysis on the initialized service data attribute through a first service data attribute analysis layer of a frequent item service data mining model to obtain an attribute component of the initialized service data attribute;
performing service data attribute association on the attribute component through a first attribute association layer of the frequent service data mining model to obtain a first service data attribute;
the attribute reorganization unit is further configured to:
extracting the service data attribute characteristics of the target frequent item service data in the target service data according to the first service data attribute through a first service data characteristic convolution layer of a frequent item service data mining model;
and clustering the service data attribute characteristics of the target frequent item service data to obtain initialized service data attribute characteristics for representing the target frequent item service data.
7. The traffic data mining device of claim 6, wherein the attribute reorganization unit is further configured to:
inputting the initialized service data attribute and the first service data attribute into a second service data attribute analysis layer of a frequent item service data mining model;
adjusting the network parameters of the second service data attribute analysis layer according to the initialized service data attribute and the first service data attribute characteristics to obtain characteristic-adjusted network parameters;
performing attribute recombination on the initialized service data attribute and the first service data attribute to obtain a recombined service data attribute;
performing first frequent item service data mining analysis on the recombined service data attribute according to the network parameter after the characteristic adjustment to obtain an attribute component corresponding to the recombined service data attribute;
and performing service data attribute association on the attribute component through a second attribute association layer of the frequent service data mining model to obtain a second service data attribute.
8. The traffic data mining device of claim 7, wherein the attribute feature filtering unit is further configured to:
acquiring a characteristic topological mapping relation of the characteristic filtered service data attribute characteristics in frequent service data distribution corresponding to the first service data attribute;
and sequentially arranging the service data attribute features after the feature filtering according to the feature topological mapping relation to obtain the service data attribute features after sequential arrangement.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113592035A (en) * | 2021-08-23 | 2021-11-02 | 广州梦源信息科技有限公司 | Big data mining method based on AI auxiliary decision and AI auxiliary decision system |
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