CN113918785A - Enterprise data analysis method based on cluster ensemble learning - Google Patents

Enterprise data analysis method based on cluster ensemble learning Download PDF

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CN113918785A
CN113918785A CN202111182846.8A CN202111182846A CN113918785A CN 113918785 A CN113918785 A CN 113918785A CN 202111182846 A CN202111182846 A CN 202111182846A CN 113918785 A CN113918785 A CN 113918785A
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程良伦
郑达成
张伟文
陈武兴
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Abstract

The invention discloses an enterprise data analysis method based on cluster ensemble learning, which comprises the following steps: s1, obtaining data of an industry to be analyzed, and finding out a plurality of major enterprises to be analyzed in the industry to be analyzed; this example analyzes the marine industry in the Guangzhou region. S2, crawling relevant data of an enterprise to be analyzed; s3, preprocessing the crawled data, and sorting the preprocessed data into a data set; s4, adopting KMeans as a base clustering device, and performing clustering ensemble learning on the data set to obtain a basic clustering result; s5, constructing a joint matrix by using the basic clustering result; and S6, processing the combined matrix by adopting single-link hierarchical clustering to obtain a final clustering integration result of the enterprise to be analyzed. The method has the characteristics of accuracy, stability and robustness, convenience in calculation and high practicability.

Description

Enterprise data analysis method based on cluster ensemble learning
Technical Field
The invention relates to the technical field of information processing, in particular to an enterprise data analysis method based on cluster ensemble learning.
Background
In current enterprise data analysis methods, data clustering or unsupervised learning is an important but extremely difficult problem. Structures in data are organized or discovered by dividing a set of unlabeled objects into homogeneous groups or clusters, such as data mining, information retrieval image segmentation, and machine learning. In real-world problems, clusters may appear in different shapes, sizes, data sparsity, and degrees of separation. In addition, noise in the data can mask the true underlying structures present in the data. Clustering techniques require defining similarity measures between patterns, which are not easily specified without any a priori knowledge of the cluster shape. Clustering integration is an algorithm for improving the accuracy, stability and robustness of clustering results, and a better result can be generated by integrating a plurality of base clustering results.
For example, chinese patent publication No. CN 112800165B, patent publication No. 2021.08.27, discloses an industry cluster positioning method based on a clustering algorithm, which obtains real-time enterprise data of a plurality of enterprises in a preset area, and classifies the plurality of enterprises according to the industry label information, so as to solve the problem that the traditional industry cluster positioning method cannot accurately position an industry cluster in a large range. However, the results analyzed by the clustering algorithm have poor accuracy, stability and robustness, and cannot well reflect the clustering results among enterprises.
Disclosure of Invention
In order to solve the problem that the analysis result of the prior art has poor accuracy, stability and robustness, the invention provides an enterprise data analysis method based on cluster ensemble learning, which has the characteristics of accuracy, stability and robustness, convenience in calculation and strong practicability.
In order to achieve the purpose of the invention, the technical scheme is as follows:
an enterprise data analysis method based on cluster ensemble learning comprises the following steps:
s1, obtaining data of an industry to be analyzed, and finding out a plurality of major enterprises to be analyzed in the industry to be analyzed;
s2, crawling relevant data of an enterprise to be analyzed;
s3, preprocessing the crawled data and sorting the processed data into a data set;
s4, adopting KMeans as a base clustering device, and performing clustering ensemble learning on the data set to obtain a basic clustering result;
s5, constructing a joint matrix by using the basic clustering result;
and S6, processing the combined matrix by adopting single-link hierarchical clustering to obtain a final clustering integration result of the enterprise to be analyzed.
Preferably, step S7 is further included after step S6: and (4) visualizing the clustering integration result by combining the related data of the enterprise to be analyzed.
Further, in step S2, the relevant data of the enterprise to be analyzed includes the operation area, the geographical location, and the registered capital of the enterprise.
Further, the preprocessing includes removing noise data, removing duplicate data, and then performing feature screening.
Further, in step S3, the step of sorting the processed data into a data set includes the following specific steps:
s301, dividing industries to be analyzed into a plurality of industry categories according to the operation field of the industries to be analyzed;
s302, coding the enterprise to be analyzed by using a one-hot method according to the corresponding industry type of the enterprise to be analyzed;
and S303, arranging the codes of the enterprises to be analyzed into a data set.
Furthermore, in S4, a KMeans clustering algorithm is used as a basis clustering device to perform cluster ensemble learning on the data set, and the specific steps are as follows:
s401, randomly selecting a value from the interval [2, 2c ], assigning the value to K, wherein c is the number of real clusters;
s402, randomly selecting K samples from a data set as initial K centroid vectors:
{μ1，μ2，…，μK}；
s403, iterating the K samples to obtain a family partition C;
further, in S403, the iteration is performed on the K samples, and the specific steps are as follows:
m1. initializing Cluster partition C to
Figure BDA0003297968730000021
Wherein t is { t belongs to N |1 and is not more than t and not more than K };
m2. for i ═ 1, 2, …, m, sample x is calculatediAnd each centroid vector mujIs a distance of
Figure BDA0003297968730000022
Figure BDA0003297968730000023
j is 1, 2, …, K, xiMinimum mark is dijCorresponding class λi(ii) a At this time, update is performed
Figure BDA0003297968730000024
M3. for j ═ 1, 2, …, K, for CjAveraging all the sample points in the image, and recalculating new centroid
Figure BDA0003297968730000031
M4. if all K centroid vectors have not changed, the output cluster division C ═ C1，C2，…，CK}。
Further, the step S5 specifically includes:
s501, generating N basic partitions P through N iterations by using a KMeans clustering algorithm:
Figure BDA0003297968730000032
wherein
Figure BDA0003297968730000033
Represents a jth cluster that is an ith partition;
s502, constructing a matrix of m by m, namely C, according to the number m of samplesm×m＝0；
And S503, obtaining a final joint matrix by using a voting method.
Further, the voting formula in S503 is:
Figure BDA0003297968730000034
wherein n isijIndicating the assignment of sample (i, j) to base partition PiNumber of clusters in which P isiRefer to
Figure BDA0003297968730000035
To a certain partition in the group.
Further, the step S6 is to process the association matrix by single-link hierarchical clustering, specifically including the steps of;
s601, calculating the distance between the data point of each category and all the data points according to the joint matrix to determine the similarity between the data points;
s602, combining two data points or categories with the nearest distance to generate a clustering tree;
s603, hierarchical clustering is carried out according to a threshold value to obtain a final partition
Figure BDA0003297968730000036
The invention has the following beneficial effects:
on the basis of the existing clustering algorithm, the KMeans is used as a base clustering device to perform clustering ensemble learning on enterprise related data to obtain a clustering ensemble result of an enterprise.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Fig. 2 is a sample point of a cluster integration result.
Fig. 3 is a sample gray image of the cluster integration result.
FIG. 4 is a sample dendrogram of cluster integration results.
Fig. 5 is a visual image of the cluster integration result.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Example 1
As shown in fig. 1, an enterprise data analysis method based on cluster ensemble learning includes the following steps:
s1, obtaining data of an industry to be analyzed, and finding out a plurality of major enterprises to be analyzed in the industry to be analyzed; the embodiment analyzes the marine industry in Guangdong province by crawling the operation field, registered capital and other data related to the marine enterprises in Guangdong province on the internet by using the popular python libraries beautiiful Soup, urllib and the like.
S2, crawling relevant data of an enterprise to be analyzed;
s3, preprocessing the crawled data and sorting the processed data into a data set;
s4, adopting KMeans as a base clustering device, and performing clustering ensemble learning on the data set to obtain a basic clustering result;
s5, constructing a joint matrix by using the basic clustering result;
and S6, processing the combined matrix by adopting single-link hierarchical clustering to obtain a final clustering integration result of the enterprise to be analyzed.
At step S2, the relevant data of the enterprise to be analyzed includes the operation area, the geographical location, and the registered capital of the enterprise.
The embodiment can use the libraries such as the Beautiful Soup and the url lib which are popular nowadays to crawl data such as the operation field, the registered capital, the geographic position and the like related to the ocean enterprises in Guangdong province on the network. The data crawled from the internet comprises a plurality of fields such as the operation field, registered capital, geographical position and the like related to the ocean enterprises in Guangdong province, and the data have the defect of more redundancy. Therefore, a certain rule is required to be set for preprocessing data according to project requirements, wherein the preprocessing comprises the steps of removing noise data and removing repeated data, and then a data set of the Guangdong province ocean enterprise is finally constructed through feature screening. In step S3, the step of sorting the processed data into a data set includes the following specific steps:
s301, dividing industries to be analyzed into a plurality of industry categories according to the operation field of the industries to be analyzed; in this embodiment, enterprises are classified into 11 categories according to the fields of aquaculture industry, cruise industry, energy industry, engineering industry, equipment industry, aquaculture industry, logistics industry, ship industry, trade industry, transportation industry, and others.
S302, coding the enterprise to be analyzed by using a one-hot method according to the corresponding industry type of the enterprise to be analyzed;
and S303, arranging the codes of the enterprises to be analyzed into a data set.
In S4, a KMeans clustering algorithm is used as a basis clustering device to perform cluster ensemble learning on the data set, as shown in fig. 2, 3 and 4Clustering integrationIn order to construct an EAC clustering integration model, firstly, a KMeans clustering method is adopted to generate a basic clustering result, and for a given data sample set, the KMeans clustering algorithm divides the sample set into K clusters according to the distance between samples, namely, K clustering centers are selected. The points in the clusters are connected together as closely as possible, and the distance between the clusters is as large as possible; in general, K can be determined by elbow rules or contour coefficients, but in order to increase diversity in cluster integration, K is { K ∈ N |2 ≦ K ≦ 2c }, and c is the number of true class clusters. Assume that input sample set D ═ x1，x2，…，xmThe output cluster division C ═ C1，C2，…，CKWherein m is the number of samples, and K is the number of clusters, the specific steps are as follows:
s401, randomly selecting a value from the interval [2, 2c ], assigning the value to K, wherein c is the number of real clusters;
s402, randomly selecting K samples from a data set as initial K centroid vectors:
{μ1，μ2，…，μK}；
s403, iterating the K samples to obtain a family partition C;
in S403, iterating the K samples, specifically including:
m1. initializing Cluster partition C to
Figure BDA0003297968730000051
Wherein t is { t belongs to N |1 and is not more than t and not more than K };
m2. for i ═ 1, 2, …, m, sample x is calculatediAnd each centroid vector mujIs a distance of
Figure BDA0003297968730000052
Figure BDA0003297968730000053
j is 1, 2, …, K, xiMinimum mark is dijCorresponding class λi(ii) a At this time, update is performed
Figure BDA0003297968730000054
M3. for j ═ 1, 2, …, K, for CjAveraging all the sample points in the image, and recalculating new centroid
Figure BDA0003297968730000055
M4. if all K centroid vectors have not changed, the output cluster division C ═ C1，C2，…，CK}。
Step S5, specifically:
s501, generating N basic partitions through N iterations by using a KMeans clustering algorithm
Figure BDA0003297968730000067
Figure BDA0003297968730000061
Wherein
Figure BDA0003297968730000062
Is represented by the i-thThe jth cluster of each partition;
s502, constructing a matrix of m by m, namely C, according to the number m of samplesm×m＝0；
And S503, obtaining a final joint matrix by using a voting method.
The voting formula in S503 is:
Figure BDA0003297968730000063
wherein n isiiIndicating the assignment of sample (i, j) to base partition PiNumber of clusters in which P isiRefer to
Figure BDA0003297968730000064
To a certain partition in the group.
Step S6 is to process the joint matrix by single-link hierarchical clustering, and the single-link hierarchical clustering algorithm combines two most similar data points of all data points by calculating the similarity between the two types of data points, and iterates this process repeatedly. The merging algorithm of single-link hierarchical clustering determines the similarity between the data points of each category and all the data points by calculating the distance between the data points, wherein the smaller the distance is, the higher the similarity is, and two data points or categories with the closest distance are combined to generate a clustering tree; in the experiment, from Cm×mAs input, hierarchical clustering is performed according to a threshold to obtain a final partition
Figure BDA0003297968730000065
The specific steps are as follows;
s601, calculating the distance between the data point of each category and all the data points according to the joint matrix to determine the similarity between the data points;
s602, combining two data points or categories with the nearest distance to generate a clustering tree;
s603, hierarchical clustering is carried out according to a threshold value to obtain a final partition
Figure BDA0003297968730000066
Step S7 is also included after step S6: the cluster integration result is visualized by combining the relevant data of the enterprise to be analyzed, and the cluster integration result is visualized as shown in fig. 5 in this embodiment. In the embodiment, on the basis of the existing clustering algorithm, the relevant data of the marine enterprise to be analyzed is crawled, the relevant data is subjected to denoising processing and is divided into 11 types, and the data sets are sorted by a one-hot method; according to the method, KMeans are used as a base clustering device, clustering ensemble learning is carried out on enterprise related data of a marine enterprise, N times of iteration is carried out on K samples, a final partition is finally obtained through single-link hierarchical clustering, a clustering ensemble result of the enterprise is obtained, and the clustering ensemble result is visualized; the method is convenient to calculate and high in practicability, and solves the problem that the result analyzed by the prior art is poor in accuracy, stability and robustness.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. An enterprise data analysis method based on cluster ensemble learning is characterized in that: the method comprises the following steps:
s1, obtaining data of an industry to be analyzed, and finding out a plurality of major enterprises to be analyzed in the industry to be analyzed;
s2, crawling relevant data of an enterprise to be analyzed;
s3, preprocessing the crawled data, and sorting the preprocessed data into a data set;
s4, adopting KMeans as a base clustering device, and performing clustering ensemble learning on the data set to obtain a basic clustering result;
s5, constructing a joint matrix by using the basic clustering result;
and S6, processing the combined matrix by adopting single-link hierarchical clustering to obtain a final clustering integration result of the enterprise to be analyzed.
2. The method of analyzing enterprise data based on cluster ensemble learning of claim 1, wherein: step S7 is also included after step S6: and (4) visualizing the clustering integration result by combining the related data of the enterprise to be analyzed.
3. The method of analyzing enterprise data based on cluster ensemble learning of claim 2, wherein: at step S2, the relevant data of the enterprise to be analyzed includes the operation area, the geographical location, and the registered capital of the enterprise.
4. The method of cluster ensemble learning based enterprise data analysis according to claim 3, wherein: the preprocessing comprises the steps of firstly removing noise data and repeated data, and then carrying out feature screening.
5. The method of cluster ensemble learning based enterprise data analysis according to claim 4, wherein: in step S3, the sorting of the preprocessed data into a data set includes the following specific steps:
s301, dividing industries to be analyzed into a plurality of industry categories according to the operation field of the industries to be analyzed;
s302, coding the enterprise to be analyzed by using a one-hot method according to the corresponding industry type of the enterprise to be analyzed;
and S303, arranging the codes of the enterprises to be analyzed into a data set.
6. The method of cluster ensemble learning based enterprise data analysis according to claim 5, wherein: in the step S4, a KMeans clustering algorithm is used as a basis clustering device to perform cluster ensemble learning on the data set, and the method specifically comprises the following steps:
s401, randomly selecting a value from the interval [2, 2c ], assigning the value to K, wherein c is the number of real clusters;
s402, randomly selecting K samples from the data set as initial K centroid directionsQuantity: { mu. }1，μ2，…，μK}；
And S403, iterating the K samples to obtain the family division C.
7. The method of cluster ensemble learning based enterprise data analysis according to claim 6, wherein: in S403, iterating the K samples, specifically including:
m1. initializing Cluster partition C to
Figure FDA0003297968720000021
Wherein t is { t belongs to N |1 and is not more than t and not more than K };
m2. for i ═ 1, 2, …, m, sample x is calculatediAnd each centroid vector mujIs a distance of
Figure FDA0003297968720000022
Figure FDA0003297968720000023
X is to beiMinimum mark is dijCorresponding class λi(ii) a At this time, update is performed
Figure FDA0003297968720000024
M3. for j ═ 1, 2, …, K, for CjAveraging all the sample points in the image, and recalculating new centroid
Figure FDA0003297968720000025
M4. if all K centroid vectors have not changed, the output cluster division C ═ C1，C2，…，CK}。
8. The method of cluster ensemble learning based enterprise data analysis according to claim 7, wherein: step S5, specifically:
s501, using a KMeans clustering algorithm to iterate for N timesGenerating N base partitions
Figure FDA0003297968720000028
Figure FDA0003297968720000026
Wherein
Figure FDA0003297968720000027
Represents a jth cluster that is an ith partition;
s502, constructing a matrix of m by m, namely C, according to the number m of samplesm×m＝0；
And S503, obtaining a final joint matrix by using a voting method.
9. The method of cluster ensemble learning based enterprise data analysis according to claim 8, wherein: the voting formula in S503 is:
Figure FDA0003297968720000031
wherein n isijIndicating the assignment of sample (i, j) to base partition PiNumber of clusters in which P isiRefer to
Figure FDA0003297968720000033
To a certain partition in the group.
10. The method of cluster ensemble learning based enterprise data analysis according to claim 8, wherein: step S6, the concrete steps of processing the association matrix by adopting single-link hierarchical clustering are as follows;
s601, calculating the distance between the data point of each category and all the data points according to the joint matrix to determine the similarity between the data points;
s602, combining two data points or categories with the nearest distance to generate a clustering tree;
s603, hierarchical clustering is carried out according to a threshold value to obtain a final partition
Figure FDA0003297968720000032
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