Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams
High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone prope...
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| Vydané v: | International journal of computational intelligence systems Ročník 8; číslo 3; s. 517 - 529 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Springer Netherlands
01.06.2015
Springer Nature B.V Springer |
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| ISSN: | 1875-6891, 1875-6883, 1875-6883 |
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| Abstract | High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone property among the utilities of itemsets, utility mining becomes a big challenge. Moreover, discovering useful patterns from the huge number of potential patterns is a mining bottleneck. However, the compact (Closed and Maximal) high utility pattern mining moderately lessens the number of patterns, but it does not solve it. Recently, an efficient framework called GUIDE, was proposed in the literature to address this issue. Though, GUIDE effectively reduced the number of high utility patterns, yet the quality of few mined patterns and their utilities are not exact. In view of this, we propose a modified MGUIDE
LM
algorithm to improve the quality and determine exact utilities of maximal patterns. |
|---|---|
| AbstractList | High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone property among the utilities of itemsets, utility mining becomes a big challenge. Moreover, discovering useful patterns from the huge number of potential patterns is a mining bottleneck. However, the compact (Closed and Maximal) high utility pattern mining moderately lessens the number of patterns, but it does not solve it. Recently, an efficient framework called GUIDE, was proposed in the literature to address this issue. Though, GUIDE effectively reduced the number of high utility patterns, yet the quality of few mined patterns and their utilities are not exact. In view of this, we propose a modified MGUIDE algorithm to improve the quality and determine exact utilities of maximal patterns. High utility pattern mining is an emerging research topic in the data mining field. Unlike frequent pattern mining, high utility pattern mining deals with non-binary databases, in which the information about purchased quantities of items is maintained. Due to the non-existence of anti-monotone property among the utilities of itemsets, utility mining becomes a big challenge. Moreover, discovering useful patterns from the huge number of potential patterns is a mining bottleneck. However, the compact (Closed and Maximal) high utility pattern mining moderately lessens the number of patterns, but it does not solve it. Recently, an efficient framework called GUIDE, was proposed in the literature to address this issue. Though, GUIDE effectively reduced the number of high utility patterns, yet the quality of few mined patterns and their utilities are not exact. In view of this, we propose a modified MGUIDE LM algorithm to improve the quality and determine exact utilities of maximal patterns. |
| Author | Om, Hari Manike, Chiranjeevi |
| Author_xml | – sequence: 1 givenname: Chiranjeevi surname: Manike fullname: Manike, Chiranjeevi email: chiru.research@gmail.com organization: Department of Computer Science and Engineering, Indian School of Mines – sequence: 2 givenname: Hari surname: Om fullname: Om, Hari organization: Department of Computer Science and Engineering, Indian School of Mines |
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| Cites_doi | 10.1016/j.eswa.2012.01.072 10.1145/1835804.1835839 10.1016/j.datak.2005.10.004 10.1016/j.datak.2007.06.009 10.1145/2396761.2396773 10.1145/1774088.1774436 10.1016/j.ins.2012.05.015 10.1016/j.eswa.2012.05.035 10.1007/978-3-540-68125-0_50 10.1007/11540007_67 10.1007/978-3-642-01307-2_76 10.1016/j.eswa.2010.12.082 10.1007/11430919_79 |
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| References_xml | – reference: Y. C. Yeh, L. J. S., and C. C. Chang, “Efficient algorithm for mining share-frequent itmesets,” In Proc.of the 11th Int. Fuzzy Systems Association World Congress, 1,(2005). – reference: https://doi.org/http://fimi.ua.ac.be/data/. – reference: C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong and Y.-K. Lee, “An efficient candidate pruning technique for high utility pattern mining,” Advances in Knowledge Discovery and Data Mining, 749–756 (2009). – reference: H. Yao, H. J. Hamilton and C. J. Butz, “A foundational approach to mining itemset utilities from databases,” SIAM Int. conf. on data mining, 482–486 (2004). – reference: V. S. Tseng, C.-W. Wu, B.-E. Shie and P. S. Yu, “UP-Growth: an efficient algorithm for high utility item-set mining,” Proc. of 16th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, 253–262 (2010). – reference: Y. C. Li, J. S. Yeh and C. C. Chang, “A fast algorithm for mining share-frequent itemsets,” In Web Technologies Research and Development-APWeb, 417–428 (2005). – reference: B.-E. Shie, V. S. Tseng and P. S. Yu, “Online mining of temporal maximal utility itemsets from data streams,” In Proc. of the 2010 ACM Symposium on Applied Computing, 1622–1626 (2010). – reference: H.-F. Li, H.-Y. Huang, Y.-C. Chen, Y.-J. Liu and S.-Y. Lee, “Fast and memory efficient mining of high utility itemsets in data streams,” In Proc. of IEEE Int. Con. on Data Mining, 881–886. (2008). – reference: LinM-YTuT-FHsuehS-CHigh utility pattern mining using the maximal itemset property and lexicographic tree structuresInformation Sciences2012215114 – reference: https://doi.org/http://www.almaden.ibm.com/software/projects/hdb/resources.shtml. – reference: A. Erwin, R. P. Gopalan and N. Achuthan, “Efficient mining of high utility itemsets from large datasets,” Advances in Knowledge Discovery and Data Mining, 554–561 (2008). – reference: C. F. Ahmed, S. K. Tanbeer and B.-S. Jeong, “Efficient mining of high utility patterns over data streams with a sliding window method,” In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 99–113 (2010). – reference: A. Erwin, R. P. Gopalan and N. Achuthan, “CTU-Mine: An efficient high utility itemset mining algorithm using the pattern growth approach,” In Proc. of IEEE Int. Conf. on Computer and Information Technology, 71–76 (2007). – reference: V. S. Tseng, C.-J. Chu and T. Liang, “Efficient mining of temporal high utility itemsets from data streams,” Second Int. Workshop on Utility-Based Data Mining, 18, (2006). – reference: AgrawalRSrikantRSrikant,”Fast algorithms for mining association rules,”In Proc. Int. Conf. Very Large Data Bases, VLDB19941215487499 – reference: LiY-CYehJ-SChangC-CIsolated items discarding strategy for discovering high utility itemsetsData and Knowledge Engineering2008641198217 – reference: C.-W. Lin, T.-P. Hong and W.-H. Lu, “An effective tree structure for mining high utility itemsets,” Expert Systems with Applications, 38(6), 7419–7424 (2011). – reference: Y. Liu, W.-k. Liao and A. Choudhary,”A two-phase algorithm for fast discovery of high utility itemsets,” In Advances in Knowledge Discovery and Data Mining, 689–695 (2005). – reference: ShieB-EYuPSTsengVSEfficient algorithms for mining maximal high utility itemsets from data streams with different modelsExpert Systems with Applications201239171294712960 – reference: N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, “Discovering frequent closed itemsets for association rules,” In Database TheoryICDT99, 398–416 (1999). – reference: YaoHHamiltonHJMining itemset utilities from transaction databasesData and Knowledge Engineering2006593603626 – reference: C.-W. Lin, G.-C. Lan and T.-P. Hong, “An incremental mining algorithm for high utility itemsets,” Expert Systems with Applications, 39(8), 7173–7180 (2012). – reference: ErwinAGopalanRPAchuthanNRA bottom-up projection based algorithm for mining high utility itemsetsIn Proc. of the 2nd Int. workshop on Integrating artificial intelligence and data mining200784311 – reference: Y. -C. Li, J. S. Yeh and C. C. Chang, “Direct candidates generation: a novel algorithm for discovering complete share-frequent itemsets,” Fuzzy Systems and Knowledge Discovery, 551–560, (2005). – reference: M. Liu, J. Qu, “Mining high utility itemsets without candidate generation,” Proc. of ACM Int. Conf. on Information and knowledge management, 55–64 (2012). – ident: cit0011 doi: 10.1016/j.eswa.2012.01.072 – start-page: 482 year: 2004 ident: cit0001 publication-title: SIAM Int. conf. on data mining – ident: cit0009 doi: 10.1145/1835804.1835839 – ident: cit0004 doi: 10.1016/j.datak.2005.10.004 – ident: cit0007 doi: 10.1016/j.datak.2007.06.009 – volume: 84 start-page: 3 year: 2007 ident: cit0022 publication-title: In Proc. of the 2nd Int. workshop on Integrating artificial intelligence and data mining – volume: 1215 start-page: 487 year: 1994 ident: cit0002 publication-title: In Proc. Int. Conf. Very Large Data Bases, VLDB – start-page: 398 year: 1999 ident: cit0019 publication-title: In Database TheoryICDT99 – start-page: 71 year: 2007 ident: cit0005 publication-title: In Proc. of IEEE Int. Conf. on Computer and Information Technology – start-page: 55 year: 2012 ident: cit0013 publication-title: Proc. of ACM Int. Conf. on Information and knowledge management doi: 10.1145/2396761.2396773 – volume: 18 year: 2006 ident: cit0014 publication-title: Second Int. Workshop on Utility-Based Data Mining – start-page: 881 year: 2008 ident: cit0015 publication-title: In Proc. of IEEE Int. Con. on Data Mining – ident: cit0016 doi: 10.1145/1774088.1774436 – start-page: 99 year: 2010 ident: cit0017 publication-title: In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing – volume: 1 year: 2005 ident: cit0021 publication-title: In Proc.of the 11th Int. Fuzzy Systems Association World Congress – ident: cit0012 doi: 10.1016/j.ins.2012.05.015 – ident: cit0018 doi: 10.1016/j.eswa.2012.05.035 – start-page: 554 year: 2008 ident: cit0006 publication-title: Advances in Knowledge Discovery and Data Mining doi: 10.1007/978-3-540-68125-0_50 – start-page: 417 year: 2005 ident: cit0020 publication-title: In Web Technologies Research and Development-APWeb – ident: cit0023 doi: 10.1007/11540007_67 – start-page: 749 year: 2009 ident: cit0008 publication-title: Advances in Knowledge Discovery and Data Mining doi: 10.1007/978-3-642-01307-2_76 – ident: cit0010 doi: 10.1016/j.eswa.2010.12.082 – ident: cit0003 doi: 10.1007/11430919_79 |
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| SubjectTerms | Algorithms Anti-monotone property Data mining Data transmission High utility patterns Maximal Patterns Pattern analysis Research Article Transaction projection Utilities |
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| Title | Modified GUIDE (LM) algorithm for mining maximal high utility patterns from data streams |
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