Mining Closed Itemsets for Coherent Rules: An Inference Analysis Approach

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Mr. Kalli Srinivasa Nageswara Prasad
Mr. Kalli Srinivasa Nageswara Prasad
σ
Prof. S.Ramakrishna
Prof. S.Ramakrishna
α Sri Venkateswara University Sri Venkateswara University

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Mining Closed Itemsets for Coherent Rules: An Inference Analysis Approach

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Abstract

Past observations have shown that a frequent item set mining algorithm are alleged to mine the closed ones because the finish offers a compact and a whole progress set and higher potency. Anyhow, the most recent closed item set mining algorithms works with candidate maintenance combined with check paradigm that is dear in runtime likewise as area usage when support threshold is a smaller amount or the item sets gets long. Here, we show, PEPP with inference analysis that could be a capable approach used for mining closed sequences for coherent rules while not candidate. It implements a unique sequence closure checking format with inference analysis that based mostly on Sequence Graph protruding by an approach labeled “Parallel Edge projection and pruning” in brief will refer as PEPP. We describe a novel inference analysis approach to prune patterns that tends to derive coherent rules. A whole observation having sparse and dense real-life information sets proved that PEPP with inference analysis performs larger compared to older algorithms because it takes low memory and is quicker than any algorithms those cited in literature frequently.

References

37 Cites in Article
  1. F Masseglia,F Cathala,P Poncelet (1995). The psp approach for mining sequential patterns.
  2. R Srikant,R (1996). Mining sequential patterns: Generalizations and performance improvements.
  3. J Han,J Pei,B Mortazavi-Asl,Q Chen,U Dayal,M Hsu (2000). FreeSpan: Frequent patternprojected sequential pattern mining.
  4. M Zaki (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences.
  5. J Pei,J Han,B Mortazavi-Asl,Q Chen,U Dayal,M Hsu (2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth.
  6. Jay Ayres,Jason Flannick,Johannes Gehrke,Tomi Yiu (2002). Sequential PAttern mining using a bitmap representation.
  7. M Garofalakis,R Rastogi,K Shim (1999). Mining sequential patterns with regular expression constraints.
  8. J Pei,J Han,W Wang (2002). Constraint-based sequential pattern mining in large databases.
  9. Masakazu Seno,George Karypis (2002). SLPMiner: An Algorithm for Finding Frequent Sequential Patterns Using Length-Decreasing Support Constraint.
  10. H Mannila,H Toivonen,A Verkamo (1995). Discovering frequent episodes in sequences.
  11. B Ozden,S Ramaswamy,A Silberschatz (1998). Cyclic association rules.
  12. C Bettini,X Wang,S Jajodia (1998). Mining temporal relationals with multiple granularities in time sequences.
  13. J Han,G Dong,Y Yin (1999). Efficient mining of partial periodic patterns in time series database.
  14. J Yang,P Yu,W Wang,J Han (2002). Mining long sequential patterns in a noisy environment.
  15. Nicolas Pasquier,Yves Bastide,Rafik Taouil,Lotfi Lakhal (1999). Discovering Frequent Closed Itemsets for Association Rules.
  16. Mohammed Zaki,Ching-Jui Hsiao (2002). CHARM: An Efficient Algorithm for Closed Itemset Mining.
  17. J Yan,R Han,Afshar (2003). CloSpan: Mining Closed Sequential Patterns in Large Databases.
  18. Jianyong Wang,Jiawei Han,Jian Pei (2003). CLOSET+.
  19. Rakesh Agrawal,Ramakrishnan Srikant (1994). Whither Data Mining?.
  20. J Pei,J Han,R Mao (2001). CLOSET: An efficient algorithm for mining frequent closed itemsets.
  21. J Han,J Wang,Y Lu,P Tzvetkov (2002). Mining Top-K Frequent Closed Patterns without Minimum Support.
  22. P Aloy,E Querol,F Aviles,M Sternberg (2002). Automated Structure-based Prediction of Functional Sites in Proteins: Applications to Assessing the Validity of Inheriting Protein Function From Homology in Genome Annotation and to Protein Docking.
  23. R Agrawal,R Srikant (1995). Mining sequential patterns.
  24. Inge Jonassen,John Collins,Desmond Higgins (1995). Finding flexible patterns in unaligned protein sequences.
  25. R Kohavi,C Brodley,B Frasca,L Mason,Z Zheng (2000). KDD-cup 2000 organizers' report: Peeling the Onion.
  26. Jianyong Wang,Jiawei Han BIDE: Efficient Mining of Frequent Closed Sequences.
  27. Kalli Srinivasa,Nageswara Prasad,Prof Ramakrishna (2011). Article: Frequent Pattern Mining and Current State of the Art.
  28. Rakesh Agrawal,Tomasz Imieliński,Arun Swami (1993). Mining association rules between sets of items in large databases.
  29. U Fayyad,G Piatetsky-Shapiro,P Smyth,R Uthurusamy (1996). Advances in Knowledge Discovery and Data Mining.
  30. A Silberschatz,A Tuzhilin (1996). What makes patterns interesting in knowledge discovery systems.
  31. Mohammed Zaki,Srinivasan Parthasarathy,Mitsunori Ogihara,Wei Li (1998). Parallel Algorithms for Discovery of Association Rules.
  32. D Burdick,M Calimlim,J Flannick,J Gehrke,T Yiu (2005). Mafia: A Maximal Frequent Itemset Mining Closed Itemsets for Coherent Rules: An Inference Analysis Approach.
  33. J Li (2006). On Optimal Rule Discovery.
  34. M Zaki (2000). Generating Non-Redundant Association Rules.
  35. N Pasquier,Y Bastide,R Taouil,L Lakhal (1999). Efficient Mining of Association Rules Using Closed Itemset Lattices.
  36. H Toivonen,M Klemettinen,P Ronkainen,K Hatonen,H Mannila (1995). Pruning and Grouping of Discovered Association Rules.
  37. B Baesens,S Viaene,J Vanthienen (2000). Post-Processing of Association Rules.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Mr. Kalli Srinivasa Nageswara Prasad. 1970. \u201cMining Closed Itemsets for Coherent Rules: An Inference Analysis Approach\u201d. Unknown Journal GJCST Volume 11 (GJCST Volume 11 Issue 19): .

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v1.2

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November 11, 2011

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en
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Past observations have shown that a frequent item set mining algorithm are alleged to mine the closed ones because the finish offers a compact and a whole progress set and higher potency. Anyhow, the most recent closed item set mining algorithms works with candidate maintenance combined with check paradigm that is dear in runtime likewise as area usage when support threshold is a smaller amount or the item sets gets long. Here, we show, PEPP with inference analysis that could be a capable approach used for mining closed sequences for coherent rules while not candidate. It implements a unique sequence closure checking format with inference analysis that based mostly on Sequence Graph protruding by an approach labeled “Parallel Edge projection and pruning” in brief will refer as PEPP. We describe a novel inference analysis approach to prune patterns that tends to derive coherent rules. A whole observation having sparse and dense real-life information sets proved that PEPP with inference analysis performs larger compared to older algorithms because it takes low memory and is quicker than any algorithms those cited in literature frequently.

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Mining Closed Itemsets for Coherent Rules: An Inference Analysis Approach

Mr. Kalli Srinivasa Nageswara Prasad
Mr. Kalli Srinivasa Nageswara Prasad Sri Venkateswara University
Prof. S.Ramakrishna
Prof. S.Ramakrishna

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