Improved Algorithm for Prediction of Heart Disease Using Case based Reasoning Technique on Non-Binary Datasets
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2012, Vol 1, Issue 7
Abstract
Frequent itemset mining is a basic problem in data mining and knowledge discovery. The discovered patterns can be used as input for Association and Classification. Association Rules and Classification Rules have been extensively studied in the literature for their usefulness in many application domains such as diagnosis, decision support, telecommunication, intrusion detection. Most of the algorithms are based on Binary data only. This paper proposes a new algorithm for generation of frequent itemsets on non-binary datasets, which are in turn used for prediction using A. We observed that this technique is an improvement over the other algorithms both in time and space.
Authors and Affiliations
Chandra Shekar Kutur, K. Ravi Kanth, K Sree Kanth
Keywords
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