Adaptive Apriori Algorithm for Frequent Itemset Mining

被引：0

作者：

Patill, Shubhangi D. ^{[1
]}

Deshmukh, Ratnadeep R. ^{[2
]}

Kirange, D. K. ^{[3
]}

机构：

[1] Govt Polytech, Dept Informat Technol, Jalgaon, India

[2] Dr Babasaheb Ambedkar Marathwada Univ, Dept Comp Sci & IT, Aurangabaad, India

[3] JT Mahcyan Coll Engn, Dept Comp Engn, Faizpur, India

来源：

PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON SYSTEM MODELING & ADVANCEMENT IN RESEARCH TRENDS (SMART-2016) | 2016年

关键词：

Database Scans; Apriori; Data Mining;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Obtaining frequent itemsets from the dataset is one of the most promising area of data mining. The Apriori algorithm is one of the most important algorithm for obtaining frequent itemsets from the dataset. But the algorithm fails in terms of time required as well as number of database scans. Hence a new improved version of Apriori is proposed in this paper which is efficient in terms of time required as well as number of database scans than the Apriori algorithm. It is well known that the size of the database for defining candidates has great effect on running time and memory need. We presented experimental results, showing that the proposed algorithm always outperform Apriori. To evaluate the performance of the proposed algorithm, we have tested it on Turkey student's database as well as a real time dataset.

引用

页码：7 / 13

页数：7

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