Fast Mining of Global Maximum Frequent Itemsets

Mining maximum frequent itemsets is a key problem in data mining field with numerous important applications. The existing algorithms of mining maximum frequent itemsets are based on local databases, and very little work has been done in distributed databases. However, using the existing algorithms for the maximum frequent itemsets or using the algorithms proposed for the global frequent itemsets needs to generate a lots of candidate itemsets and requires a large amount of communication overhead. Therefore, this paper proposes an algorithm for fast mining global maximum frequent itemsets (FMGMFI), which can conveniently get the global frequency of any itemset from the corresponding paths of every local FP-tree by using frequent pattern tree and require far less communication overhead by the searching strategy of bottom-up and top-down. Experimental results show that FMGMFI is effective and efficient.