A Study and Analysis of Association Rule Mining Algorithms In Data Mining

Authors

  • N. Yuvamathi  PG Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
  • R. Porkodi  Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Keywords:

Data Mining, Association Rule, Apriori, FP-Growth, Tertius

Abstract

The data mining is a technology that has been developed rapidly. It is based on complex algorithms that allow for the segmentation of data to identify pattern and trends, detect anomalies, and predict the probability of various situational outcomes. The raw data being mined may come in both analog and digital formats depending on the data sources. There are many trends that are available in data mining some of the new trends are Distributed Data Mining (DDM), Multimedia Data Mining, Spatial and Geographic Data Mining, Time Series and Sequence Data Mining, Time Series and Sequence Data Mining . This paper is based on Association rule mining. In the field of association rule mining, many algorithms exist for exploring the relationships among the items in the database. These algorithms are very much different from one another and take different amount of time to execute on the same sets of data. In this paper, a sample dataset has been taken and various association rule mining algorithms namely Apriori, FPGrowth, Tertius have been compared. The algorithms of association rule mining are discussed and analyzed deeply. The main objective of this paper is to present a review on the basic concepts of ARM technique and its algorithms.

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International Journal of Scientific Research in Science and Technology

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Published

2018-02-28

Issue

Volume 4, Issue 2, January-February-2018

Section

Research Articles

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How to Cite

[1]
N. Yuvamathi, R. Porkodi, " A Study and Analysis of Association Rule Mining Algorithms In Data Mining, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 2, pp.283-289, January-February-2018.