An Improved Single and Multiple Association Approach for Mining Medical Databases

The main aim of data mining is to extract useful patterns from huge amount of data. For this purpose some effective techniques like Apriori algorithm is presented. The major drawback of Apriori algorithm is Generate huge candidate sets: 104 frequent 1-itemset will generate 107 candidate 2-itemsets.To discover a frequent pattern of size 100, e.g., {a1, a2, …, a100}, one needs to generate 2100 which is approx. 1030 candidates. Candidate Test incurs multiple scans of database: each candidate. To remove the above drawback, we present an improved non candidate single and multiple association approach for mining medical databases. The developed approach generates association rules for determining the relationships among the diseases observed synchronously. The generated association rules are too significant for making early diagnosis for the correlated diseases. Some types of diseases can have triggering effects on different kinds of diseases. The symptoms and diseases which have stronger effect on each other can be determined and interpreted by the constructed system and the large and extended databases can be scanned effectively with the pruning property of the developed system.