Novelty framework for knowledge discovery in databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3181, Issue , 2004, Pages 48-57

  Al Hegami, Ahmed Sultan ^a   Bhatnagar, Vasudha ^a   Kumar, Naveen ^a  

^a UNIVERSITY OF DELHI   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA WAREHOUSES; DATABASE SYSTEMS; ITERATIVE METHODS;

DATA MINING ALGORITHM; DATA MINING PROBLEMS; HIDDEN PATTERNS; INTERESTINGNESS; ITERATIVE PROCESS; KNOWLEDGE DISCOVERY IN DATABASE; OBJECTIVE MEASURE; REDUCED COMPLEXITY;

DATA MINING;

EID: 35048860455     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30076-2_5     Document Type: Article

References (19)

1. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining, Edited by U. M. Fayyad , G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, Menlo Park, CA:AAAI/MIT Press. (1996).
2. Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, San Francisco, CA, USA. (1999).
3. Duda, R. O., Hart, P. E., Stork, D. G.: Pattern Classification. 2nd Edition, John Wiley & Sons (Asia) PV. Ltd. (2002).
4. Liu, B., Hsu, W., Chen, S.: Using General Impressions to Analyze Discovered Classification Rules. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD 97). (1997).
5. Piateskey-Shapiro, G., Matheus, C. J.: The Interestingness of Deviations. In Proceedings of AAAI Workshop on Knowledge Discovery in Databases. (1994).
6. Liu, B., Hsu, W., Mun, L., Lee, H.: Finding Interesting Patterns Using User Expectations. Technical Report:TRA7/96. Department of Information Systems and Computer Science, National University of Singapore (1996).
7. Silberschatz, A., Tuzhilin, A.: On Subjective Measures of Interestingness in Knowledge Discovery. In Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining. (1995).
8. Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. In IEEE Transactions on Knowledge and Data Engineering. V.5, No.6. (1996).
9. Liu, B., Hsu, W.: Post Analysis of Learned Rules. In Proceedings of the 13th National Conference on AI(AAAI'96). (1996).
10. Padmanabhan, B., Tuzhilin, A.: Unexpectedness as a Measure of Interestingness in Knowledge Discovery. Working paper # IS-97-6, Dept. of Information Systems, Stern School of Business, NYU. (1997).
11. Kohonen, T.: Self-Organization and Associative Memory. 3rd edition, Springer, Berlin. (1993).
12. Yairi, T., Kato, Y., Hori, K.: Fault Detection by Mining Association Rules from House-keeping Data. In Proceedings of International Symposium on Artificial Intelligence, Robotics and Automation in Space (SAIRAS 2001).
13. Marsland, S.: On-Line Novelty Detection Through Self-Organization, with Application to Robotics. Ph.D. Thesis, Department of Computer Science, University of Manchester. (2001).
14. Basu, S., Mooney, R. J., Pasupuleti, K. V., Ghosh, J.: Using Lexical Knowledge to Evaluate the Novelty of Rules Mined from Text. In Proceedings of the NAACL workshop and other Lexical Resources: Applications, Extensions and Customizations. (2001).
15. Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD98). (1998).
16. Pujari, A. K.: Data Mining Techniques. 1st Edition, Universities Press(India) Limited. (2001).
17. Dunham, M. H.: Data Mining: Introductory and Advanced Topics. 1st Edition, Pearson Education (Singaphore) Pte. Ltd. (2003).
18. Williams, G. J.: Evolutionary Hot Spots Data Mining: An Architecture for Exploring for Interesting Discoveries. In Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD99). (1999).
19. Psaila, G.: Discovery of Association Rule Meta-Patterns. In Proceedings of 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK99). (1999).