MODIFIED ALGORITHM FOR FINDING FREQUENT ITEMSETS

Computer Engineering

Data mining is a process of extraction of valuable and unknown information from the large databases. The information can be converted into knowledge about historical patterns. Many Algorithms have been proposed to mine association rule that uses support and confidence as constraint. Association rules mining is used to find frequent pattern and correlation existed in item sets through data processing, analysis, synthesis and inference. Association rules mining has achieved a very great application effect in business and other fields and it has become a research hotpot. In the field of association rules mining, Apriori algorithm is most popular. It is based on frequent item sets generation algorithm. Apriori algorithm is based on breadth-first search and its data structure is simple, clear and easy to understand. But, the application of Apriori algorithm needs to scan database many times which leads to a great overhead. so we tries to improve efficiency of this algorithm by removing this limitations.

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