Research Article Open Access

A Descriptive Framework for the Multidimensional Medical Data Mining and Representation

Veeramalai Sankaradass and Kannan Arputharaj


Problem statement: Association rule mining with fuzzy logic was explored by research for effective datamining and classification. Approach: It was used to find all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Results: In this study, we propose new rule mining technique using fuzzy logic for mining medical data in order to understand and better serve the needs of Multidimensional Breast cancer Data applications. Conclusion: The main objective of multidimensional Medical data mining is to provide the end user with more useful and interesting patterns. Therefore, the main contribution of this study is the proposed and implementation of fuzzy temporal association rule mining algorithm to classify and detect breast cancer from the dataset.

Journal of Computer Science
Volume 7 No. 4, 2011, 519-525


Submitted On: 15 February 2011 Published On: 4 April 2011

How to Cite: Sankaradass, V. & Arputharaj, K. (2011). A Descriptive Framework for the Multidimensional Medical Data Mining and Representation. Journal of Computer Science, 7(4), 519-525.

  • 5 Citations



  • Data discretization
  • fuzzy logic
  • Association Rule Mining (ARM)
  • Minimum Description Length (MDL)
  • medical data mining
  • multidimensional data