Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions
People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been growing in recent years. Many algorithms de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Islamic Azad University, Bandar Abbas Branch
2022-11-01
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| Series: | Transactions on Fuzzy Sets and Systems |
| Subjects: | |
| Online Access: | https://tfss.journals.iau.ir/article_691317_3a9ddfad42914073275ec02bdcd98ad9.pdf |
| _version_ | 1797827894618095616 |
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| author | Tzung-Pei Hong Chun-Hao Chen Yan-Kang Li Min-Thai Wu |
| author_facet | Tzung-Pei Hong Chun-Hao Chen Yan-Kang Li Min-Thai Wu |
| author_sort | Tzung-Pei Hong |
| collection | DOAJ |
| description | People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been growing in recent years. Many algorithms dealt with concept-drift information and detected new market trends. This paper proposes an approach based on fuzzy c-means (FCM) to mine the concept drift of fuzzy membership functions. The proposed algorithm is subdivided into two stages. In the first stage, individual fuzzy membership functions are generated from different training databases by the proposed FCM-based approach. Then, the proposed algorithm will mine the concept-drift patterns from the sets of fuzzy membership functions in the second stage. Experiments on simulated datasets were also conducted to show the effectiveness of the approach. |
| first_indexed | 2024-04-09T12:55:42Z |
| format | Article |
| id | doaj.art-22567d8316b745c4a4b1447edd3175a9 |
| institution | Directory Open Access Journal |
| issn | 2821-0131 |
| language | English |
| last_indexed | 2024-04-09T12:55:42Z |
| publishDate | 2022-11-01 |
| publisher | Islamic Azad University, Bandar Abbas Branch |
| record_format | Article |
| series | Transactions on Fuzzy Sets and Systems |
| spelling | doaj.art-22567d8316b745c4a4b1447edd3175a92023-05-13T17:27:53ZengIslamic Azad University, Bandar Abbas BranchTransactions on Fuzzy Sets and Systems2821-01312022-11-0112213110.30495/tfss.2022.1958730.1030691317Using Fuzzy C-means to Discover Concept-drift Patterns for Membership FunctionsTzung-Pei Hong0Chun-Hao Chen1Yan-Kang Li2Min-Thai Wu3Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.Department of Information and Finance Management, National Taipei University of Technology, Taipei, Taiwan.Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan.College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, China.People often change their minds at different times and at different places. It is important and valuable to indicate concept-drift patterns in unexpected ways for shopping behaviours for commercial applications. Research about concept drift has been growing in recent years. Many algorithms dealt with concept-drift information and detected new market trends. This paper proposes an approach based on fuzzy c-means (FCM) to mine the concept drift of fuzzy membership functions. The proposed algorithm is subdivided into two stages. In the first stage, individual fuzzy membership functions are generated from different training databases by the proposed FCM-based approach. Then, the proposed algorithm will mine the concept-drift patterns from the sets of fuzzy membership functions in the second stage. Experiments on simulated datasets were also conducted to show the effectiveness of the approach.https://tfss.journals.iau.ir/article_691317_3a9ddfad42914073275ec02bdcd98ad9.pdfconcept driftdata miningfuzzy c-meansmembership function |
| spellingShingle | Tzung-Pei Hong Chun-Hao Chen Yan-Kang Li Min-Thai Wu Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions Transactions on Fuzzy Sets and Systems concept drift data mining fuzzy c-means membership function |
| title | Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions |
| title_full | Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions |
| title_fullStr | Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions |
| title_full_unstemmed | Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions |
| title_short | Using Fuzzy C-means to Discover Concept-drift Patterns for Membership Functions |
| title_sort | using fuzzy c means to discover concept drift patterns for membership functions |
| topic | concept drift data mining fuzzy c-means membership function |
| url | https://tfss.journals.iau.ir/article_691317_3a9ddfad42914073275ec02bdcd98ad9.pdf |
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