Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Obstacles and misunderstandings facing medical data mining
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Uniqueness of medical data mining
Artificial Intelligence in Medicine
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Comprehensibility is driving force in medical data mining results since doctors utilize the outputs and give the final decision. Another important issue specific to some data sets, like physical activity, is their uniform distribution due to tile analysis that was performed on them In this paper, we propose a novel data mining tool named SDI (Shape Distribution Indicator) to give a comprehensive view of co-relations of attributes together with an index named ISDI to show the robustness of SDI outputs. We apply SDI to explore the relationship of the Physical Activity data and symptoms in medical test dataset for which popular data mining methods fail to give an appropriate output to help doctors decisions. In our experiment, SDI found several useful relationships.