Abstract
Frequent itemsets, also called frequent patterns, are important information about databases, and mining efficiently frequent itemsets is a core problem in data mining area. Pattern growth approaches, such as the classic FP-Growth algorithm and the efficient FPgrowth* algorithm, can solve the problem. The approaches mine frequent itemsets by constructing recursively conditional databases that are usually represented by prefix-trees. The three major costs of such approaches are prefix-tree traversal, support counting, and prefix-tree construction. This paper presents a novel pattern growth algorithm called BFP-growth in which the three costs are greatly reduced. We compare the costs among BFP-growth, FP-Growth, and FPgrowth*, and illuminate that the costs of BFP-growth are the least. Experimental data show that BFP-growth outperforms not only FP-Growth and FPgrowth* but also several famous algorithms including dEclat and LCM, ones of the fastest algorithms, for various databases.
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Qu, JF., Liu, M. (2012). A High-Performance Algorithm for Frequent Itemset Mining. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds) Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32281-5_8
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DOI: https://doi.org/10.1007/978-3-642-32281-5_8
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