New incremental updating algorithm for mining association rules based on AprioriTidList algorithm
Liu Han-bing, Zhang Ya-juan, Zheng Quan-lu, Ye Mao-gong
Abstract
Liu Han-bing, Zhang Ya-juan, Zheng Quan-lu, Ye Mao-gong
Abstract
In this paper, a new efficient incremental updating algorithm for mining association rules (ATLUP) is proposed, which resolves the problem of updating the association rules when increasing transaction database without changing the minimum support and minimum confidence. Main features of this algorithm are that: frequent itemsets of new transaction database are produced by AprioriTidList algorithm, and candidate itemsets are classified and pruned in effective ways. Therefore, the time of scanning former and new database is reduced to once, efficiency of updating association rules is improved. Experimental result shows the feasibility and effectiveness of this algorithm.
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In this paper, a new efficient incremental updating algorithm for mining association rules (ATLUP) is proposed, which resolves the problem of updating the association rules when increasing transaction database without changing the minimum support and minimum confidence. Main features of this algorithm are that: frequent itemsets of new transaction database are produced by AprioriTidList algorithm, and candidate itemsets are classified and pruned in effective ways. Therefore, the time of scanning former and new database is reduced to once, efficiency of updating association rules is improved. Experimental result shows the feasibility and effectiveness of this algorithm.
Key concepts: Association rule learning, Database transaction, Computer science, Data mining, GSP Algorithm, Algorithm, Algorithm design, Efficient algorithm