文章基本信息

标题：FREQUENT ITEMSET MINING ALGORITHMS: A SURVEY
本地全文：下载
作者：Sireesha Moturi ; Dr.S.N.TirumalaRao ; Dr. Srikanth Vemuru 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：3
页码：744
出版社：Journal of Theoretical and Applied
摘要：Task of extracting fruitful knowledge from huge datasets is called data mining. It has several aspects like predictive modeling or classification, cluster analysis, association analysis, anomaly detection and regression etc. Among all association rule mining is one of the major tasks for data mining. Association analysis is mainly used to discover patterns, which describes strongly associated features in the data. Market basket data is one of the major applications of association rule mining. Other applications include bioinformatics, medical diagnosis, scientific data analysis, web mining, finding the relationships between different elements of earth climate system etc. Various algorithms have been proposed by researchers to improve the performance of frequent pattern mining such as Apriori, Frequent Pattern (FP)-growth etc. We are providing a brief description of some of the techniques in detail in the later section of this paper.
关键词：Association Rule Mining; Support; Confidence; Frequent Itemset