MFCD:An Optimized Technique for Mining Frequent Closed Item Sets

The amount of data being collected is increasing rapidly. The main reason is the use of computerized applications. Because of that reason the valuable information is hidden in large amount of data. It is attracting researchers of multiple disciplines to study effective approaches to derive useful knowledge from vast data .Frequent closed item set mining has been a heart favorite theme for data mining researchers for over a decade. A large amount of literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent closed item set mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this thesis, we propose a new technique for more efficient frequent closed item set mining. Our proposed algorithm will reduce the complexity of frequent closed item set mining. We present efficient techniques to implement the new approach.