Improved Apriori Algorithm for Mining Association Rules
Автор: Darshan M. Tank
Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs
Статья в выпуске: 7 Vol. 6, 2014 года.
Бесплатный доступ
Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2- itemsets, the poor efficiency of counting their support. Proposed algorithm reduces one redundant pruning operations of C_2. If the number of frequent 1-itemsets is n, then the number of connected candidate 2-itemsets is C-n, while pruning operations C_n. The proposed algorithm decreases pruning operations of candidate 2-itemsets, thereby saving time and increasing efficiency. For the bottleneck: poor efficiency of counting support, proposed algorithm optimizes subset operation, through the transaction tag to speed up support calculations. Algorithm Apriori is one of the oldest and most versatile algorithms of Frequent Pattern Mining (FPM). Its advantages and its moderate traverse of the search space pay off when mining very large databases. Proposed algorithm improves Apriori algorithm by the way of a decrease of pruning operations, which generates the candidate 2-itemsets by the apriori-gen operation. Besides, it adopts the tag-counting method to calculate support quickly. So the bottleneck is overcome.
Association rule mining, frequent itemset generation, support and confidence
Короткий адрес: https://sciup.org/15012111
IDR: 15012111
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