Max-Miner Algorithm Using Knowledge Discovery Process in Data Mining

Discovering frequent item sets is an important key problem in data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. Typical algorithms for solving this problem operate in a bottom-up, breadth-first search direction. The computation starts from frequent itemsets (the minimum length frequent itemsets) and continues until all maximal (length) frequent itemsets were found. During the execution, every frequent item set is explicitly considered. A new algorithm is presented which combines both the bottom-up and the topdown searches. The primary search direction is still bottom-up, but a restricted search is also conducted in the top-down direction. This search is used only for maintaining and updating a new data structure, the maximum frequent candidate set. It is used to prune early candidates that would normally encountered in the bottom-up search. A very important characteristic of the algorithm is that it does not require explicit examination of every frequent item set. Therefore the algorithm performs well even when some maximal frequent item sets are long. As its output, the algorithm produces the maximum frequent set, i.e., the set containing all maximal frequent item sets, thus specifying immediately all frequent item sets. Pattern-mining algorithm (Max-Miner) presented scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriority scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnitude or more.