A Unified Approach for Discovery of Interesting Association Rules in Medical Databases

Association rule discovery is an important technique for mining knowledge from large databases. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules and to improve the overall efficiency of the knowledge discovery in databases process (KDD). The objective of this paper is to provide a framework that uses subjective measures of interestingness to discover interesting patterns from association rules algorithms. The framework works in an environment where the medical databases are evolving with time. In this paper we consider a unified approach to quantify interestingness of association rules. We believe that the expert mining can provide a basis for determining user threshold which will ultimately help us in finding interesting rules. The framework is tested on public datasets in medical domain and results are promising.

[1]  Gregory Piatetsky-Shapiro,et al.  Discovery, Analysis, and Presentation of Strong Rules , 1991, Knowledge Discovery in Databases.

[2]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[3]  Balaji Padmanabhan,et al.  A Belief-Driven Method for Discovering Unexpected Patterns , 1998, KDD.

[4]  Wynne Hsu,et al.  Analyzing the Subjective Interestingness of Association Rules , 2000, IEEE Intell. Syst..

[5]  Balaji Padmanabhan,et al.  Small is beautiful: discovering the minimal set of unexpected patterns , 2000, KDD '00.

[6]  Wynne Hsu,et al.  Using General Impressions to Analyze Discovered Classification Rules , 1997, KDD.

[7]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[8]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[9]  Dino Pedreschi,et al.  ExAMiner: optimized level-wise frequent pattern mining with monotone constraints , 2003, Third IEEE International Conference on Data Mining.

[10]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[11]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[12]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[13]  Giuseppe Psaila.  Discovery of Association Rule Meta-Patterns , 1999, DaWaK.

[14]  Wynne Hsu,et al.  Finding Interesting Patterns Using User Expectations , 1999, IEEE Trans. Knowl. Data Eng..

[15]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[16]  Einoshin Suzuki,et al.  Discovery of Surprising Exception Rules Based on Intensity of Implication , 1998, PKDD.

[17]  Naveen Kumar,et al.  Novelty Framework for Knowledge Discovery in Databases , 2004, DaWaK.

[18]  Dino Pedreschi,et al.  ExAnte: Anticipated Data Reduction in Constrained Pattern Mining , 2003, PKDD.

[19]  Abraham Silberschatz,et al.  On Subjective Measures of Interestingness in Knowledge Discovery , 1995, KDD.

[20]  Simon Parsons,et al.  Principles of Data Mining by David J. Hand, Heikki Mannila and Padhraic Smyth, MIT Press, 546 pp., £34.50, ISBN 0-262-08290-X , 2004, The Knowledge Engineering Review.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[22]  Balaji Padmanabhan,et al.  Unexpectedness as a Measure of Interestingness in Knowledge Discovery , 1999, Decis. Support Syst..

[23]  Wynne Hsu,et al.  Post-Analysis of Learned Rules , 1996, AAAI/IAAI, Vol. 1.

[24]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[25]  Evangelos Triantaphyllou,et al.  Interactive Learning of Monotone Boolean Functions , 1996, Inf. Sci..

[26]  Dino Pedreschi,et al.  Adaptive Constraint Pushing in Frequent Pattern Mining , 2003, PKDD.

[27]  Mengchi Liu,et al.  Design and Implementation of the ROL Deductive Object-Oriented Database System , 2000, Journal of Intelligent Information Systems.

[28]  Alex Alves Freitas,et al.  On rule interestingness measures , 1999, Knowl. Based Syst..

[29]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .