Article

Approaches for Mining Frequent Itemsets and Minimal Association Rules

Author(s):

Prajakta R. Tanksali, Padre Conceicao College of Engineering, Verna, Goa, India

Keywords

Itemset, Frequent Itemset, Support Count/ Threshold, Support, Confidence, Association Rules.

Abstract

Frequent itemsets mining is a popular and common concept used in day-to-day life in many application areas including Web usage mining, intrusion detection, and bioinformatics etc. This is the right place were the various Frequent Itemsets Mining Algorithms are used, which help the store manager/in-charge to arrange these items in a particular fashion so that the number of items purchased by the customers increase, thereby increasing the sales of the store. Such information can be used as the basis for decisions about marketing activities such as, promotional offers, seasonal offers or product placements. This paper presents a literature study of the different approaches to achieve the goal of frequent itemsets mining. We have tried to design an application for a chemist using these algorithms on a medical pharmacy dataset to help the shop owner maintain his stocks well and as per the user requirements.

Reference

    [1] Mingjun Song and Sanguthevar Rajasekaran, “A Transaction Mapping Algorithm for Frequent Itemsets Mining”, IEEE Transactions On Knowledge And Data Engineering, VOL. 18, NO. 4, APRIL 2006. [2] Christian Borgelt,”An Implementation of the FP-growth Algorithm”. [3] Jiawei Han und Micheline Kamber, “Frequent Item set Mining Methods”. [4] S. Neelima, N. Satyanarayana and P. Krishna Murthy, “A Survey on Approaches for Mining Frequent Itemsets”. [5] Pratima Gautam, Dr. K. R. Pardasani, “Algorithm for Efficient Multilevel Association Rule Mining”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 05, 2010, 1700-1704. [6] Ya-Han Hu, Fan Wu, Yi-Jiun Liao, ‘An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports’, The Journal of Systems and Software 86 (2013) 1224–1238. [7] Ke-Chung Lin, I-En Liao, Tsui-Ping Chang, Shu-Fan Lin, “A frequent itemset mining algorithm based on the Principle of Inclusion–Exclusion and transaction mapping”. [8] Trieu Anh Tuan Ritsumeikan University 2012, “A Vertical Representation for Parallel dEclat Algorithm in Frequent Itemset Mining”. [9] Paresh Tanna, Dr. Yogesh Ghodasara, “Using Apriori with WEKA for Frequent Pattern Mining”. [10] Kanu Patel, Vatsal Shah, Jitendra Patel, Jayna Donga, “Comparison of Various Association Rule Mining Algorithm on Frequent Itemsets”.

Other Details

Paper ID: GRDJEV01I070095
Published in: Volume : 1, Issue : 7
Publication Date: 2016-07-01
Page(s): 88 - 92

Article Preview

Download   Google Scholar