Improvised apriori algorithm using frequent pattern tree for real time applications in data mining

被引：46

作者：

Bhandari, Akshita ^{[1
]}

Gupta, Ashutosh ^{[1
]}

Das, Debasis ^{[1
]}

机构：

[1] Niit Univ, Dept Comp Sci & Engn, Neemrana 301705, Rajasthan, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES, ICICT 2014 | 2015年 / 46卷

关键词：

Apriori; Improvised Apriori; Minimum Support; Minimum Confidence; Itemsets; Frequent itemsets; Candidate itemsets; Frequent Pattern tree; Conditional patterns; Time and Space Complexity;

D O I：

10.1016/j.procs.2015.02.115

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Apriori Algorithm is one of the most important algorithm which is used to extract frequent itemsets from large database and get the association rule for discovering the knowledge. It basically requires two important things: minimum support and minimum confidence. First, we check whether the items are greater than or equal to the minimum support and we find the frequent itemsets respectively. Secondly, the minimum confidence constraint is used to form association rules. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time and space for scanning the whole database searching on the frequent itemsets, and present an improvement on Apriori. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：644 / 651

页数：8

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