CN114548225B - Method, device and equipment for processing situation data outlier sample based on FCM - Google Patents
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Abstract
The application relates to a situation data outlier sample processing method, device, computer equipment and storage medium based on FCM. The method comprises the following steps: the situation data samples are divided into normal samples and outlier samples through an outlier detection algorithm based on a frequent mode, the normal samples are classified through a fuzzy linear discrimination algorithm, the outlier samples are classified through a fuzzy C-means clustering algorithm, and the initial clustering center of the outlier samples is taken from the class center obtained through a fuzzy K nearest neighbor algorithm, so that the clustering of the outlier samples has prior information of normal sample classification in advance, and the recognition effect of the situation data containing the outlier sample data is improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and apparatus for processing an outlier sample of situation data based on FCM, a computer device, and a storage medium.
Background
Modern war is an integrated combined combat under informatization conditions, needs to be developed under an integrated combat situation, and is critical to form the integrated combat situation in real time. The future general situation map has the characteristics of informatization, intellectualization and the like, and can provide the functions of situation comprehensive research and judgment, situation cognition and the like. The situation elements refer to forces, environments, events, estimates and other elements forming the situation. Different situations mean that they contain different situation elements. Although the current information data acquisition means are numerous, the data volume is also larger and larger, but the data are faced with more complex quality problems. Under some strong countermeasure conditions, the environment is bad, various sensors return data which are affected by environmental factors such as geography, climate, hydrology, electromagnetism and the like, noise and errors are easy to exist, and the sensor belongs to weak labeling data in practice. The weak labeling sample causes a large number of outlier samples, and is difficult to directly apply traditional time sequence analysis, supervised learning and semi-supervised learning methods to support situation analysis.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a FCM-based situation data outlier sample processing method, apparatus, computer device, and storage medium capable of identifying situation data containing outlier samples.
An FCM-based situation data outlier sample processing method, the method comprising:
acquiring situation data samples, and acquiring normal samples and outlier samples according to the situation data samples through an outlier detection algorithm based on a frequent mode;
classifying the normal samples through a fuzzy linear discrimination algorithm, and determining initial generic information and an initial clustering center of clustering;
determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information;
obtaining an adjusted cluster center according to the membership matrix and the initial cluster center;
classifying the outlier samples by a fuzzy C-means clustering algorithm according to the adjusted clustering center as an initial clustering center of the outlier samples to obtain classification results of the outlier samples;
and merging the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
In one embodiment, the method further comprises: calculating Euclidean distance between any two samples in the situation data samples to form a distance matrix;
changing the diagonal value in the distance matrix into inf;
the distance matrix is sequenced in an ascending order according to columns to obtain k samples nearest to each sample, and the category information of each of the k samples is determined according to the initial category information;
and determining the membership degree of each sample to each category according to a preset membership degree calculation formula according to the k samples nearest to each sample and the category information thereof.
In one embodiment, the method further comprises: the calculation formula of the membership degree is as follows:
wherein mu ij Indicating the membership degree of the jth sample to the ith class, wherein gamma is a preset adjustable parameter, and n ij The number of samples belonging to the ith class in the k adjacent points of the jth sample is represented.
In one embodiment, the method further comprises: obtaining an adjusted cluster center according to the membership matrix and the initial cluster center; wherein, the adjustment formula is:
wherein ω' i I=1, 2, C is the initial cluster center, ω i I=1, 2,..c is the adjusted cluster center, x j The point corresponding to the j-th sample is represented, C is the total number of the genera, and M is the total number of the samples.
In one embodiment, the method further comprises: the situation data comprises the following steps: at least one of battlefield force element information, battlefield environment element information, and battlefield event element information.
An FCM-based situation data outlier sample processing apparatus, the apparatus comprising:
the situation data acquisition module is used for acquiring situation data samples, and acquiring normal samples and outlier samples through an outlier detection algorithm based on a frequent mode according to the situation data samples;
the normal sample processing module is used for classifying the normal samples through a fuzzy linear discrimination algorithm and determining initial generic information and an initial clustering center of clustering;
the membership matrix determining module is used for determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information;
the cluster center adjusting module is used for obtaining an adjusted cluster center according to the membership matrix and the initial cluster center;
the outlier sample processing module is used for classifying the outlier samples through a fuzzy C-means clustering algorithm according to the adjusted clustering center serving as an initial clustering center of the outlier samples to obtain classification results of the outlier samples;
and the identification result output module is used for combining the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring situation data samples, and acquiring normal samples and outlier samples according to the situation data samples through an outlier detection algorithm based on a frequent mode;
classifying the normal samples through a fuzzy linear discrimination algorithm, and determining initial generic information and an initial clustering center of clustering;
determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information;
obtaining an adjusted cluster center according to the membership matrix and the initial cluster center;
classifying the outlier samples by a fuzzy C-means clustering algorithm according to the adjusted clustering center as an initial clustering center of the outlier samples to obtain classification results of the outlier samples;
and merging the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring situation data samples, and acquiring normal samples and outlier samples according to the situation data samples through an outlier detection algorithm based on a frequent mode;
classifying the normal samples through a fuzzy linear discrimination algorithm, and determining initial generic information and an initial clustering center of clustering;
determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information;
obtaining an adjusted cluster center according to the membership matrix and the initial cluster center;
classifying the outlier samples by a fuzzy C-means clustering algorithm according to the adjusted clustering center as an initial clustering center of the outlier samples to obtain classification results of the outlier samples;
and merging the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
According to the FCM-based situation data outlier sample processing method, device, computer equipment and storage medium, the situation data samples are divided into the normal samples and the outlier samples through the outlier detection algorithm based on the frequent mode, the normal samples are classified through the fuzzy linear discrimination algorithm, the outlier samples are classified through the fuzzy C-means clustering algorithm, and the initial clustering center of the outlier samples is taken from the class center obtained through the fuzzy K-nearest neighbor algorithm, so that clustering of the outlier samples is advanced by prior information of normal sample classification, and the recognition effect of situation data containing outlier sample data is improved.
Drawings
FIG. 1 is a flow diagram of a method for processing an outlier sample of situation data based on FCM in one embodiment;
FIG. 2 is a block diagram of an FCM-based situation data outlier sample processing device in one embodiment;
FIG. 3 is an internal block diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a method for processing an outlier sample of situation data based on FCM, including the steps of:
step 102, acquiring situation data samples, and obtaining normal samples and outlier samples according to the situation data samples through an outlier detection algorithm based on a frequent mode.
Frequent pattern mining aims at finding frequent attributes of each subspace of the entire data space to achieve full mining of complete subspaces. By performing sensitivity analysis and correlation analysis, including classification cross-validation correct rate voting and information entropy analysis, on each subspace of the complete subspace set, a clear knowledge of the attributes of the data is obtained. Outlier samples in the situation data are determined by an outlier detection algorithm based on the frequent pattern.
And 104, classifying the normal samples through a fuzzy linear discrimination algorithm, and determining initial generic information and an initial clustering center of the clusters.
The Fuzzy linear discriminant algorithm is the Fuzzy-LDA algorithm, and LDA is to reduce the data to one dimension, and the dimension-reduced data can be distinguished as far as possible. The Fuzzy linear discriminant algorithm is the Fuzzy-LDA algorithm.
And step 106, determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information.
Specifically, calculating Euclidean distance between any two samples in the situation data samples to form a distance matrix; changing the diagonal value in the distance matrix into inf; ascending order is carried out on the distance matrix according to columns to obtain k samples nearest to each sample, and respective category information of the k samples is determined according to the initial category information; and determining the membership degree of each sample to each category according to a preset membership degree calculation formula according to k samples nearest to each sample and category information thereof. The calculation formula of the membership degree is as follows:
wherein mu ij Indicating the membership degree of the jth sample to the ith class, wherein gamma is a preset adjustable parameter, and n ij The number of samples belonging to the ith class in the k adjacent points of the jth sample is represented.
More specifically, taking γ as 0.51, the calculation formula of the membership is:
if the number of neighbors of sample j belonging to the same class is small or none, that is, n ij Small value, mu ij Near 0.51, if the sample belongs to the same class of neighbor total n ij Near k, its membership is near 1; when the sample j does not belong to the class i, the total number n of the belonging classes in the neighbor of the two samples ij Equal to k, mu ij =0.49, the likelihood that the sample belongs to class i is relatively high; when sample j belongs to neighbor number n of class i ij When=0, μ ij =0. Here 0.5 is chosen as the demarcation point, with the most ambiguities near 0.5.
And step 108, obtaining an adjusted cluster center according to the membership matrix and the initial cluster center.
Step 110, classifying the outlier samples by a fuzzy C-means clustering algorithm according to the adjusted cluster center as the initial cluster center of the outlier samples, and obtaining classification results of the outlier samples.
The fuzzy C-means clustering algorithm, namely FCM, is a dynamic clustering method, and the method can determine an objective function for evaluating the quality of a clustering result on the basis of selecting a distance measure as an inter-sample similarity measure, and simultaneously, for a given initial classification, find the best clustering result of taking an extremum of the objective function by using an iterative method.
And step 112, merging the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
In the situation data outlier sample processing method based on the FCM, the situation data samples are divided into the normal samples and the outlier samples through the outlier detection algorithm based on the frequent mode, the normal samples are classified through the fuzzy linear discrimination algorithm, the outlier samples are classified through the fuzzy C-means clustering algorithm, and the initial clustering center of the outlier samples is taken from the class center obtained through the fuzzy K-means clustering algorithm, so that the clustering of the outlier samples is advanced by the prior information of the normal sample classification, and the recognition effect of the situation data containing the outlier sample data is improved.
In one embodiment, the method further comprises: obtaining an adjusted clustering center according to the membership matrix and the initial clustering center; wherein, the adjustment formula is:
wherein ω' i I=1, 2, where, C is the initial cluster center omega i I=1, 2,..c is the adjusted cluster center, x j The point corresponding to the j-th sample is represented, C is the total number of the genera, and M is the total number of the samples.
In one embodiment, the method further comprises: the situation data includes: at least one of battlefield force element information, battlefield environment element information, and battlefield event element information.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
In one embodiment, as shown in fig. 2, there is provided an FCM-based situation data outlier sample processing apparatus, comprising: the situation data acquisition module 202, the normal sample processing module 204, the membership matrix determination module 206, the cluster center adjustment module 208, the outlier sample processing module 210 and the recognition result output module 212, wherein:
the situation data acquisition module 202 is configured to acquire a situation data sample, and obtain a normal sample and an outlier sample according to the situation data sample through an outlier detection algorithm based on a frequent mode;
the normal sample processing module 204 is configured to classify the normal samples according to a fuzzy linear discrimination algorithm, and determine initial generic information and an initial clustering center of the clusters;
the membership matrix determining module 206 is configured to determine a membership matrix of the situation data sample according to the initial generic information through a fuzzy K approximation algorithm;
the cluster center adjusting module 208 is configured to obtain an adjusted cluster center according to the membership matrix and the initial cluster center;
the outlier sample processing module 210 is configured to classify the outlier sample according to the adjusted cluster center as an initial cluster center of the outlier sample by using a fuzzy C-means clustering algorithm, so as to obtain a classification result of the outlier sample;
the recognition result output module 212 is configured to combine the classification result of the normal sample and the classification result of the outlier sample to obtain a recognition result of the situation data sample.
The membership matrix determining module 206 is further configured to calculate euclidean distance between any two samples in the situation data samples, so as to form a distance matrix; changing the diagonal value in the distance matrix into inf; ascending order is carried out on the distance matrix according to columns to obtain k samples nearest to each sample, and respective category information of the k samples is determined according to the initial category information; and determining the membership degree of each sample to each category according to a preset membership degree calculation formula according to k samples nearest to each sample and category information thereof.
The cluster center adjustment module 208 is further configured to obtain an adjusted cluster center according to the membership matrix and the initial cluster center; wherein, the adjustment formula is:
wherein ω' i I=1, 2, where, C is the initial cluster center omega i I=1, 2,..c is the adjusted cluster center, x j The point corresponding to the j-th sample is represented, C is the total number of the genera, and M is the total number of the samples.
For specific limitations on the FCM-based situation data outlier sample processing device, reference may be made to the above limitation on the FCM-based situation data outlier sample processing method, which is not described here again. The individual modules in the FCM-based situation data outlier sample processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a FCM-based situation data outlier sample processing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment a computer device is provided comprising a memory storing a computer program and a processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (4)
1. An FCM-based situation data outlier sample processing method, comprising:
acquiring situation data samples, and acquiring normal samples and outlier samples according to the situation data samples through an outlier detection algorithm based on a frequent mode; the situation data comprises the following steps: at least one of battlefield force element information, battlefield environment element information, and battlefield event element information;
classifying the normal samples through a fuzzy linear discrimination algorithm, and determining initial generic information and an initial clustering center of clustering;
determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information, wherein the steps comprise:
calculating Euclidean distance between any two samples in the situation data samples to form a distance matrix, changing diagonal values in the distance matrix into inf, and carrying out ascending sorting on the distance matrix according to columns to obtain the nearest distance to each samplekA plurality of samples and determining from the initial generic informationkThe respective category information of each sample is determined according to the nearest samplekEach sample and category information thereof, and determining the membership degree of each sample to each category according to a preset membership degree calculation formula; the calculation formula of the membership degree is as follows:
，
wherein,indicate->The individual samples are subject to->Class membership->Is a preset adjustable parameter +.>Indicate->Of individual sampleskThe nearest neighbor point belongs to +.>The number of samples of the class;
obtaining an adjusted cluster center according to the membership matrix and the initial cluster center; wherein, the adjustment formula is:
，
wherein,for the initial cluster center,/->In order for the cluster center to be adjusted,indicate->Points corresponding to the individual samples, < >>For the total number of categories>Is the total number of samples;
classifying the outlier samples by a fuzzy C-means clustering algorithm according to the adjusted clustering center as an initial clustering center of the outlier samples to obtain classification results of the outlier samples;
and merging the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
2. An FCM-based situation data outlier sample processing apparatus, the apparatus comprising:
the situation data acquisition module is used for acquiring situation data samples, and acquiring normal samples and outlier samples through an outlier detection algorithm based on a frequent mode according to the situation data samples; the situation data comprises the following steps: at least one of battlefield force element information, battlefield environment element information, and battlefield event element information;
the normal sample processing module is used for classifying the normal samples through a fuzzy linear discrimination algorithm and determining initial generic information and an initial clustering center of clustering;
the membership matrix determining module is used for determining a membership matrix of the situation data sample through a fuzzy K approximation algorithm according to the initial generic information, and the steps comprise:
calculating Euclidean distance between any two samples in the situation data samples to form a distance matrix, changing diagonal values in the distance matrix into inf, and carrying out ascending sorting on the distance matrix according to columns to obtain the nearest distance to each samplekA plurality of samples and determining from the initial generic informationkThe respective category information of each sample is determined according to the nearest samplekEach sample and category information thereof, and determining the membership degree of each sample to each category according to a preset membership degree calculation formula; the calculation formula of the membership degree is as follows:
，
wherein,indicate->The individual samples are subject to->Class membership->Is a preset adjustable parameter +.>Indicate->Of individual sampleskThe nearest neighbor point belongs to +.>The number of samples of the class;
the cluster center adjusting module is used for obtaining an adjusted cluster center according to the membership matrix and the initial cluster center; wherein, the adjustment formula is:
，
wherein,for the initial cluster center,/->In order for the cluster center to be adjusted,indicate->Points corresponding to the individual samples, < >>For the total number of categories>Is the total number of samples;
the outlier sample processing module is used for classifying the outlier samples through a fuzzy C-means clustering algorithm according to the adjusted clustering center serving as an initial clustering center of the outlier samples to obtain classification results of the outlier samples;
and the identification result output module is used for combining the classification result of the normal sample and the classification result of the outlier sample to obtain the identification result of the situation data sample.
3. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of claim 1 when executing the computer program.
4. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of claim 1.
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