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

Influence of Data Mining and Data Warehouse on Strategic Planning: A Review Paper

Users can access their data in today's reporting environment, but it does not address all of their issues. The people have access to the statistics, but they cannot ensure the data's truthfulness or the speed with which...

Handwritten Devnagari Optical Character Recognition

Handwritten Devanagari character recognition is the ability of a computer to receive and interpret handwritten input from sources such as paper documents, photographs, touch-screens and other devices. Handwritten Devanag...

Seismic Analysis of RCC Building (G+2) Using Staad Pro

Earthquakes signal a change in the earth's internal structure. Seismic activity is frequent in most places of the world, albeit the frequency with which it occurs is dependent on the tectonic configuration of the area. P...

Face Recognition Technology for Automatic Attendance System

The attendance system is essential in schools and colleges. There are several drawbacks to manual attendance systems, including the fact that they are less dependable and difficult to maintain. This enhances accuracy whi...

A Review on Compressed Sensing Space-Time Frequency Index Modulation in OFDM System

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. In space-time shift keying (STSK), the information is conveyed by both the spatial an...

Download PDF file
  • EP ID EP748142
  • DOI 10.21276/ijircst.2018.6.4.4
  • Views 29
  • 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