Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream
Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2...
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| Veröffentlicht in: | Wireless communications and mobile computing 2021, Vol.2021 (1), Article 6662254 |
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| creator | Chen, Jing Li, Peng Fang, Weiqing Zhou, Ning Yin, Yue Zheng, Hui Xu, He Wang, Ruchuan |
| description | Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP-T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates. |
| doi_str_mv | 10.1155/2021/6662254 |
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Therefore, this paper proposes a new weighted sliding window fuzzy frequent pattern mining algorithm based on interval type-2 fuzzy set theory over data stream (WSWFFP-T2) with a single scan based on the artificial datasets of medical data stream. The weighted fuzzy frequent pattern tree based on type-2 fuzzy set theory (WFFPT2-tree) and fuzzy-list sorted structure (FLSS) is designed to mine the fuzzy frequent patterns (FFPs) over the medical data stream. The experiments show that the proposed WSWFFP-T2 algorithm is optimal for mining the quantitative data stream and not limited to the fragile databases; the performance is reliable and stable under the condition of the weighted sliding window. Moreover, the proposed algorithm has high performance in mining the FFPs compared with the existing algorithms under the condition of recall and precision rates.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2021/6662254</identifier><language>eng</language><publisher>LONDON: Hindawi</publisher><subject>Algorithms ; Computer Science ; Computer Science, Information Systems ; Data mining ; Data transmission ; Datasets ; Engineering ; Engineering, Electrical & Electronic ; Fuzzy set theory ; Fuzzy sets ; Medical diagnosis ; Mines ; Pattern analysis ; Science & Technology ; Set theory ; Sliding ; Technology ; Telecommunications</subject><ispartof>Wireless communications and mobile computing, 2021, Vol.2021 (1), Article 6662254</ispartof><rights>Copyright © 2021 Jing Chen et al.</rights><rights>Copyright © 2021 Jing Chen et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>4</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000738938400003</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c337t-497f7c37b66b8b9cd1407cda30a242cc4501e08973a9af30ac4bab99ddb949a63</citedby><cites>FETCH-LOGICAL-c337t-497f7c37b66b8b9cd1407cda30a242cc4501e08973a9af30ac4bab99ddb949a63</cites><orcidid>0000-0001-8919-2528 ; 0000-0002-1927-6301 ; 0000-0001-5959-2483 ; 0000-0003-2809-2237 ; 0000-0001-5026-5347 ; 0000-0002-6044-731X ; 0000-0002-6890-6488</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,4012,27910,27911,27912</link.rule.ids></links><search><contributor>Sangaiah, Arun K.</contributor><contributor>Arun K Sangaiah</contributor><creatorcontrib>Chen, Jing</creatorcontrib><creatorcontrib>Li, Peng</creatorcontrib><creatorcontrib>Fang, Weiqing</creatorcontrib><creatorcontrib>Zhou, Ning</creatorcontrib><creatorcontrib>Yin, Yue</creatorcontrib><creatorcontrib>Zheng, Hui</creatorcontrib><creatorcontrib>Xu, He</creatorcontrib><creatorcontrib>Wang, Ruchuan</creatorcontrib><title>Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream</title><title>Wireless communications and mobile computing</title><addtitle>WIREL COMMUN MOB COM</addtitle><description>Real-time data stream mining algorithms are largely based on binary datasets and do not handle continuous quantitative data streams, especially in medical data mining field. 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| subjects | Algorithms Computer Science Computer Science, Information Systems Data mining Data transmission Datasets Engineering Engineering, Electrical & Electronic Fuzzy set theory Fuzzy sets Medical diagnosis Mines Pattern analysis Science & Technology Set theory Sliding Technology Telecommunications |
| title | Fuzzy Frequent Pattern Mining Algorithm Based on Weighted Sliding Window and Type-2 Fuzzy Sets over Medical Data Stream |
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