Discovering long maximal frequent pattern

Abstract

Association rule mining, the most commonly used method for data mining, has numerous applications. Although many approaches that can find association rules have been developed, most utilize maximum frequent itemsets that are short. Existing methods fail to perform well in applications involving large amounts of data and incur longer itemsets. Apriori-like algorithms have this problem because they generate many candidate itemsets and spend considerable time scanning databases; that is, their processing method is bottom-up and layered. This paper solves this problem via a novel hybrid Multilevel-Search algorithm. The algorithm concurrently uses the bidirectional Pincer-Search and parameter prediction mechanism along with the bottom-up search of the Parameterised method to reduce the number of candidate itemsets and consequently, the number of database scans. Experimental results demonstrate that the proposed algorithm performs well, especially when the length of the maximum frequent itemsets are longer than or equal to eight. The concurrent approach of our multilevel algorithm results in faster execution time and improved efficiency.