Analysis of An Enhanced Apriori Algorithms in Data Mining

doi:10.3969/j.issn.1671-1122.2015.11.013

Abstract

Abstract:

In the highly developed information society, network data expand rapidly and much important information hide behind the surge of data. So it is necessary that analyze a large amounts of data. Apriori algorithm is a frequent item set algorithm for mining association rules. Its core idea is to excavate frequent item sets through two stages including generating candidate sets and closed down testing of plot. May generate a large number of candidate sets and may need to repeat scanning database are the two major drawbacks of Apriori algorithm. By eliminating unnecessary transmission of records in the database, the improved Apriori algorithm effectively reduces the time spent on I/O, greatly optimizes the efficiency of the algorithm, proves and gives the algorithm implementation thought. In this paper, an enhanced Apriori algorithm is proposed which takes less scanning time. It is achieved by eliminating the redundant generation of sub-items during pruning the candidate item sets. Both traditional and enhanced Apriori algorithms are compared and analyzed in this paper.

Key words: data mining, association rule, frequent item sets, transaction number, support counting

CLC Number:

TP309

HU Xue, FENG Hua-min, LI Ming-wei, DING Zhao. Analysis of An Enhanced Apriori Algorithms in Data Mining[J]. Netinfo Security, 2015, 15(11): 77-83.

Figures/Tables 20

References 20

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