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Data mining has become an indispensable technology for businesses and researchers in many fields. Discovering frequent itemsets is a key problem in important data mining applications. Typical association algorithms for solving this problem operate in a bottom-up, top-down and breadth-first search direction. The computation starts from frequent 1-itemsets (the minimum length frequent itemsets) and continues until all maximal (length) frequent itemsets are found. Algorithms perform well when all maximal frequent itemsets are short. However, performance drastically decreases when some of the maximal frequent itemsets are relatively long. This paper focuses on finding Maximum Frequent Set with the implementation of the APRIORI and the Dynamic Itemset Counting Algorithm (DIC) and a comparative study with Pincer Search Algorithm to select the fast algorithm for discovering the Maximum Frequent Set. General Terms Knowledge Discovery, Data Warehousing, Data Mining, Algorithms, Patterns.

Runtime and Space Complexity Comparison of the various Association Algorithms