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Mining regular/frequent itemsets is very important concept in association rule mining which shows association among the variables in huge database. the classical algorithm used for extracting regular itemsets faces two fatal deficiencies .firstly it scans the database multiple times and secondly it generates large number of irregular itemsets hence increases spatial and temporal complexties and overall decreases the efficiency of classical apriori algorithm.to overcome the limitations of classical algorithm we proposed an improved algorithm in this paper with a aim of minimizing the temporal and spatial complexities by cutting off the database scans to one by generating compressed data structure bit matrix(b_matrix)-and by reducing redundant computations for extracting regular itemsets using top down method. theoritical analysis and experimental results shows that improved algorithm is better than classical apriori algorithm.
Shalini Dutt, Naveen Choudhary. 2014. "An Improved Apriori Algorithm based on Matrix Data Structure". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C5).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
v1.2
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Total Score: 143
Country: India
Subject: Global Journal of Computer Science and Technology
Authors: Shalini Dutt, Naveen Choudhary, Dharm Singh (PhD/Dr. count: 0)
View Count (all-time): 402
Total Views (Real + Logic): 3093
Total Downloads (simulated): 178
Publish Date: 2014 01, Wed
Monthly Totals (Real + Logic):
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