Regression and Correlation Analysis of Different Interestingness Measures for Mining Association Rules

Abstract

Association Rule Mining is the significant way to extract knowledge from data sets. The association among the instance of a dataset can measured with Interestingness Measures (IM) metrics. IM define how much interesting the extract knowledge is. Researchers have proved that the classical Support-Confidence metrics can’t extract the real knowledge and they have been proposing different IM. From a user perspective it’s really tough to select the minimal and best measures from them. From our experiment, the correlation among the various IM such as Support, Confidence, Lift, Cosine, Jaccard, Leverage etc. are evaluated in different popular data sets. In this paper our contribution is to find the correlation among the IM with different ranges in different types of data sets which were not applied in past researches. This study also identified that the correlation varies from data set to data set and proposed a solution based on multiple criterion that will help the users to select the minimal and best from a large number of IM.

Authors and Affiliations

Mir Md. Jahangir Kabir, Tansif Anzar,

Keywords

Related Articles

Virtual World-Based Multii-View-Point Vision Time Reliant Simulation Data

A virtual world is a computer-generated simulation of a world with specific geographical and physical qualities in which users can interact with one another through "avatars," or projections of themselves. Virtual realit...

GPU Implementation of Sales Forecasting with Linear Regression

Forecasting of sales is very important in any business as it helps managers to learn from historical data and make informed decisions. This generally involves intensive processes using spreadsheets which require inputs f...

Adaptive Generalized Predictive Control Applied to Motor Drive Axis

The topic of this article is the adaptive generalized predictive control (GPC) applied to the control of the speed of a digital axis. The system is used in CNC machine tools. Usually, the control of digital axes must obe...

Intent Search: Survey on Various Methods of Image Re-ranking

Image re-ranking is an effective way for improving the result of web-based image search. To refine the text-based image search result image search re-ranking is the best approach. In this Paper, we give the different met...

An Overview, Origins, Uses, and Difficulties of IoT

The Internet of Things (IoT) is quickly expanding nowadays. In the near future, billions of gadgets are likely to be linked. Smart and sensing devices have had an influence on Big Data by generating and collecting large...

Download PDF file
  • EP ID EP748142
  • DOI 10.21276/ijircst.2018.6.4.4
  • Views 53
  • Downloads 0

How To Cite

Mir Md. Jahangir Kabir, Tansif Anzar, (2018). Regression and Correlation Analysis of Different Interestingness Measures for Mining Association Rules. International Journal of Innovative Research in Computer Science and Technology, 6(4), -. https://europub.co.uk/articles/-A-748142