Consumers’ Purchase Behavior Preference in E-Commerce Platform Based on Data Mining Algorithm

E-commerce platform can recommend products to users by analyzing consumers’ purchase behavior preference. In the clustering process, the existing methods of purchasing behavior preference analysis are easy to fall into the local optimal problem, which makes the results of preference analysis inaccurate. Therefore, this paper proposes a method of consumer purchasing behavior preference analysis on e-commerce platform based on data mining algorithm. Create e-commerce platform user portrait template with consumer data records, select attribute variables and set value range. This paper uses data mining algorithm to extract the purchase behavior characteristics of user portrait template, takes the characteristics as the clustering analysis object, designs the clustering algorithm of consumer purchase behavior, and grasps the common points of group behavior. On this basis, the model of consumer purchase behavior preference is established to predict and evaluate the behavior preference. The experimental results show that the accuracy rate of this method is 91.74%, the recall rate is 88.67%, and the F1 value is 90.17%, which are higher than the existing methods, and can provide consumers with more satisfactory product information push.

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Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Wireless Communications and Mobile Computing ◽

10.1155/2021/8321636 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Kai Ji

Keyword(s):

Data Mining ◽

Communication Network ◽

Big Data ◽

Clustering Algorithm ◽

Personal Information ◽

Personal Communication ◽

Communication Process ◽

Data Mining Algorithm ◽

Mining Algorithm ◽

Wireless Personal Communication

Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.

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Massive Data Mining Algorithm for Web Text Based on Clustering Algorithm

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0362 ◽

2019 ◽

Vol 23 (2) ◽

pp. 362-365 ◽

Cited By ~ 1

Author(s):

Nan-Chao Luo ◽

Keyword(s):

Data Mining ◽

Vector Space ◽

Clustering Algorithm ◽

Current Data ◽

Massive Data ◽

Data Mining Algorithm ◽

Quality Of Data ◽

Chi Square ◽

Data Mining Algorithms ◽

Mining Algorithm

The massive data of Web text has the characteristics of high dimension and sparse spatial distribution, which makes the problems of low mining precision and long time consuming in the process of mining mass data of Web text by using the current data mining algorithms. To solve these problems, a massive data mining algorithm of Web text based on clustering algorithm is proposed. By using chi square test, the feature words of massive data are extracted and the set of characteristic words is gotten. Hierarchical clustering of feature sets is made, TF-IDF values of each word in clustering set are calculated, and vector space model is constructed. By introducing fair operation and clone operation on bee colony algorithm, the diversity of vector space models can be improved. For the result of the clustering center, K-means is introduced to extract the local centroid and improve the quality of data mining. Experimental results show that the proposed algorithm can effectively improve data mining accuracy and time consuming.

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A Data Mining Algorithm Based on Improved K-Means Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2028 ◽

2014 ◽

Vol 543-547 ◽

pp. 2028-2031 ◽

Cited By ~ 2

Author(s):

Yang Zheng

Keyword(s):

Data Mining ◽

Clustering Algorithm ◽

Experimental Results ◽

Data Mining Algorithm ◽

Mining Algorithm ◽

Clustering Center ◽

Minimum Sample ◽

Sample Points

This paper purposes a K-means clustering algorithm based on improved filtering process. Thealgorithm improves the filtering process,The two minimum sample points are reasonable initial clustering centers. It makes the probability summary of data in a cluster as large as possible, and the probability summary of data in different clusters as small as possible. Experimental results show that the proposed algorithm can select the proper initial clustering center, and it is more compact and robust than thetraditional K-means clustering algorithm.

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Simulation on K-Means Optimum Clustering Mining Algorithm Based on Slope Classification

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.791-793.1385 ◽

2013 ◽

Vol 791-793 ◽

pp. 1385-1388

Author(s):

Si Jin Zhou ◽

Shou Jian Wu ◽

Hao Jiang

Keyword(s):

Data Mining ◽

Clustering Algorithm ◽

Research Result ◽

Large Data ◽

Engineering Practice ◽

Data Mining Algorithm ◽

Data Simulation ◽

Genetic Method ◽

Mining Algorithm ◽

Slope Factor

The efficient data mining algorithm was researched in this paper, according to the massive data in the database, the efficiency and the fluency of the data mining should be attached much importance in the research. And yet at the same time, the precision of mining algorithm should be improved. Combing with the genetic algorithm and K-means clustering algorithm, an improved data mining algorithm was proposed. In the new algorithm, the slope factor was taken in advantage, then the phenomenon that the smaller classification caused the less optimum solution was avoided, and the defects of the two algorithms are offset. The mining simulation and experiment was taken based on the different databases with different sizes of data. Simulation result shows that the new algorithm based on the slope factor K-means clustering genetic method can solve the data mining problem for the large data base. The data mining result is much more precise than the traditional method. Research result shows the improved algorithm has predominant prospect in application, and it has good value in the engineering practice.

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