Attribute Relational Analysis (ARA) for Coherent Association Rules: A post mining process for Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset

§ Sri Venkateswara University, Tirupati

Send Message

To: Author

Attribute Relational Analysis (ARA) for Coherent Association Rules: A post mining process for Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset

Article Fingerprint

ReserarchID

REPO3U2

Attribute Relational Analysis (ARA) for Coherent Association Rules: A post mining process for Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Reading Preferences
Font Size
Line Spacing
Background
This converted HTML version may contain rendering inconsistencies. Please refer to the PDF for the authoritative version, or click here to provide feedback.

Abstract

Association rules present one of the most impressive techniques for the analysis of attribute associations in a given dataset related to applications related to retail, bioinformatics, and sociology. In the area of data mining, the importance of the rule management in associating rule mining is rapidly growing. Usually, If datasets are large, the induced rules are large in volume. The density of the rule volume leads to the obtained knowledge hard to be understood and analyze. One better way of minimizing the rule set size is eliminating redundant rules from rule base. Many efforts have been made and various competent and excellent algorithms have been proposed. But all of these models relying either on closed itemset mining or expert’s evaluation. None of these models are proven best in all data set contexts. Closed itemset model is missing adaptability and expert’s evaluation process is resulting different significance for same rule under different expert’s view. To overcome these limits here we proposed a post mining process called ARA as an extension to our earlier proposed closed itemset mining algorithm called PEPP.

References

35 Cites in Article
  1. 1. S Fienberg,G Shmueli (2005). Statistical issues and challenges associated with rapid detection of bioterrorist attacks. Statistics in Medicine
  2. 2. Pedro Carmona-Saez,Monica Chagoyen,Andres Rodriguez,Oswaldo Trelles,Jose Carazo,Alberto Pascual-Montano (2006). Integrated analysis of gene expression by association rules discovery. BMC Bioinformatics, 7(1)
  3. 3. Ronaldo Cristiano,Prati (2009). QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, 133-148.
  4. 4. P Mcnicholas (2007). Association rule analysis of CAO data (with discussion). Journal of the Statistical and Social Inquiry Society of Ireland
  5. 5. R Prati,P Flach (2005). ROCCER: an algorithm for rule learning based on ROC analysis. Proceeding of the 19th International Joint Conference on Artificial Intelligence
  6. 6. Tom Fawcett (2008). PRIE: a system for generating rulelists to maximize ROC performance. Data Mining and Knowledge Discovery, 17(2), 207-224.
  7. 7. Hiroyuki Kawano,Minoru Kawahara (2002). Extended Association Algorithm Based on ROC Analysis for Visual Information Navigator.
  8. 8. G Piatetsky-Shapiro,G Piatetsky-Shapiro,W Frawley,S Brin,R Motwani,J Ullman (2005). Discovery, Analysis, and Presentation of Strong Rules. Proceedings of the 11th international symposium on Applied Stochastic Models and Data Analysis ASMDA, 16, 191-200.
  9. 9. B Liu,W Hsu,Y Ma (1999). Pruning and summarizing the discovered associations. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 125-134.
  10. 10. Hacene Cherfi,Amedeo Napoli,Yannick Toussaint (2009). A Conformity Measure Using Background Knowledge for Association Rules: Application to Text Mining. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, 100-115.
  11. 11. B Liu,W Hsu,Y Ma (1999). Pruning and summarizing the discovered associations. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
  12. 12. Hetal Thakkar,Barzan Mozafari,Carlo Zaniolo (2009). Continuous Post-Mining of Association Rules in a Data Stream Management System. Post-Mining of Association Rules, 116-132.
  13. 13. P Kuntz,F Guillet,R Lehn,H Briand (2000). A User-Driven Process for Mining Association Rules. 4th Eur. Conf. on Principles of Data Mining and Knowledge Discovery
  14. 14. Huawen Liu,Jigui Sun,Huijie Zhang (2009). Post-Processing for Rule Reduction Using Closed Set. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, 81-99.
  15. 15. Szymon Jaroszewicz,Dan Simovici (2004). Interestingness of frequent itemsets using Bayesian networks as background knowledge. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 178-186.
  16. 16. S Jaroszewicz,T Scheffer (2005). Fast Discovery of Unexpected Patterns in Data, Relative to a Bayesian Network. ACM SIGKDD Conference on Knowledge Discovery in Databases
  17. 17. C Faure,D Delprat,J Boulicaut,A Mille (2006). Iterative Bayesian Network Implementation by using Annotated Association Rules. 15th Int'l Conf. on Knowledge Engineering and Knowledge Management -Managing Knowledge in a World of Networks, 18.
  18. 18. B Liu,W Hsu (1996). Post-Analysis of Learned Rules. AAAI/IAAI
  19. 19. B Lent,A Swami,J Widom (1997). Mining for strong negative associations in a large database of customer transactions. Proceedings of the Thirteenth International Conference on Data Engineering
  20. 20. Sugato Basu,Raymond Mooney,Krupakar Pasupuleti,Joydeep Ghosh (2001). Evaluating the novelty of text-mined rules using lexical knowledge. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 233-238.
  21. 21. Claudia Marinica,Fabrice Guillet (2010). Knowledge-Based Interactive Postmining of Association Rules Using Ontologies. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 22(6)
  22. 22. M Zaki,C Hsiao (2002). Charm: An Efficient Algorithm for Closed Itemset Mining. Proc. Second SIAM Int'l Conf. Data Mining, 34-43.
  23. 23. J Pei,J Han,R Mao (2000). Closet: An Efficient Algorithm for Mining Frequent Closed Itemsets. Proc. ACM SIGMOD Workshop Research Issues in Data Mining and Knowledge Discovery, 21-30.
  24. 24. Mohammed Zaki,Srinivasan Parthasarathy,Mitsunori Ogihara,Wei Li (1998). Parallel Algorithms for Discovery of Association Rules. Data Mining and Knowledge Discovery, 1(4), 343-373.
  25. 25. D Burdick,M Calimlim,J Flannick,J Gehrke,T Yiu (2005). Mafia: A Maximal Frequent Itemset Algorithm. IEEE Trans. Knowledge and Data Eng, 17(11), 1490-1504.
  26. 26. J Li (2006). On Optimal Rule Discovery. IEEE Trans. Knowledge and Data Eng, 18(4), 460-471.
  27. 27. M Hahsler,C Buchta,K Hornik (2008). Selective Association Rule Generation. Computational Statistic, 23(2), 303-315.
  28. 28. H Toivonen,M Klemettinen,P Ronkainen,K Hatonen,H Mannila (1995). Pruning and Grouping of Discovered Association Rules. Proc. ECML-95 Workshop Statistics, Machine Learning, and Knowledge Discovery in Databases, 47-52.
  29. 29. Roberto Bayardo,Rakesh Agrawal (1999). Mining the most interesting rules. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 145-154.
  30. 30. M Klemettinen,H Mannila,P Ronkainen,H Toivonen,A Verkamo (1994). Finding Interesting Rules from Large Sets of Discovered Association Rules. Proc. Int'l Conf. Information and Knowledge Management (CIKM), 401-407.
  31. 31. R Srikant,R (1995). Mining Generalized Association Rules. Proc. 21st Int'l Conf. Very Large Databases, 407-419.
  32. 32. Barzan Mozafari,Hetal Thakkar,Carlo Zaniolo (2008). Verifying and Mining Frequent Patterns from Large Windows over Data Streams. Proceedings of the 24th International Conference on Data Engineering (ICDE 2008)
  33. 33. Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset Mining kalli Srinivasa Nageswara Prasad. Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset Mining kalli Srinivasa Nageswara Prasad
  34. 34. S Prof,Ramakrishna (2011). Unknown Title. International Journal of Computer Science and Information Security Publication, 9(9)
  35. 35. Kalli Srinivasa,Nageswara Prasad,Prof Ramakrishna (2011). Global Journal of Computer Science and Technology. Global Journal of Computer Science and Technology, 11(19)

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

Kalli Srinivasa Nageswara Prasad, Prof. S. Ramakrishna. 2011. "Attribute Relational Analysis (ARA) for Coherent Association Rules: A post mining process for Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset". Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 11 (GJRE Volume 11 Issue I7).

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords
Classification
GJRE-I Classification FOR Code: 080109
Version of record

v1.2

Issue date
December 28, 2011

Language
English
Order Article Reprint
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 1.4K
Total Downloads: 58
All Trends

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Attribute Relational Analysis (ARA) for Coherent Association Rules: A post mining process for Parallel Edge Projection and Pruning (PEPP) Based Sequence Graph protrude approach for Closed Itemset

Kalli Prasad
Kalli Prasad Sri Venkateswara University, Tirupati
Prof. Ramakrishna
Prof. Ramakrishna