Using Clustering Analysis and Association Rule Technology in Cross-Marketing
In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applie...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9979874 |
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| _version_ | 1832566464537690112 |
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| author | Yang Cheng Ming Cheng Tao Pang Sizhen Liu |
| author_facet | Yang Cheng Ming Cheng Tao Pang Sizhen Liu |
| author_sort | Yang Cheng |
| collection | DOAJ |
| description | In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value. |
| format | Article |
| id | doaj-art-2c0b001f7a37479ba7c29a5bc3adebe1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2c0b001f7a37479ba7c29a5bc3adebe12025-02-03T01:04:05ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99798749979874Using Clustering Analysis and Association Rule Technology in Cross-MarketingYang Cheng0Ming Cheng1Tao Pang2Sizhen Liu3School of Journalism and Communication, Wuhan University, Wuhan, Hubei 430072, ChinaSchool of Journalism and Communication, Wuhan University, Wuhan, Hubei 430072, ChinaBeijing Navroom Technology Co., Ltd., Beijing 100000, ChinaChangjiang Ecological Environmental Protection Group Co., Ltd., Wuhan, Hubei 430000, ChinaIn this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.http://dx.doi.org/10.1155/2021/9979874 |
| spellingShingle | Yang Cheng Ming Cheng Tao Pang Sizhen Liu Using Clustering Analysis and Association Rule Technology in Cross-Marketing Complexity |
| title | Using Clustering Analysis and Association Rule Technology in Cross-Marketing |
| title_full | Using Clustering Analysis and Association Rule Technology in Cross-Marketing |
| title_fullStr | Using Clustering Analysis and Association Rule Technology in Cross-Marketing |
| title_full_unstemmed | Using Clustering Analysis and Association Rule Technology in Cross-Marketing |
| title_short | Using Clustering Analysis and Association Rule Technology in Cross-Marketing |
| title_sort | using clustering analysis and association rule technology in cross marketing |
| url | http://dx.doi.org/10.1155/2021/9979874 |
| work_keys_str_mv | AT yangcheng usingclusteringanalysisandassociationruletechnologyincrossmarketing AT mingcheng usingclusteringanalysisandassociationruletechnologyincrossmarketing AT taopang usingclusteringanalysisandassociationruletechnologyincrossmarketing AT sizhenliu usingclusteringanalysisandassociationruletechnologyincrossmarketing |