Analysis of Different Data Mining Tools using Classification, Clustering and Association Rule Mining
Author(s) -
Pritam H. Patil,
Suvarna Thube,
Bhakti Ratnaparkhi,
K. Rajeswari
Publication year - 2014
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/16238-5766
Subject(s) - association rule learning , computer science , data mining , cluster analysis , data science , artificial intelligence
Now days in all fields to extract useful knowledge from data, data mining techniques like classification, clustering, association rule mining are useful. In data mining classification is categorization of different objects and Clustering is methodology using which we will be able to club objects of similar type. Another methodology like association rule mining (ARM) [1] is useful to find out association relationship among different objects. This paper compares performance of different data mining tools [2] like WEKA [3] , XLMiner [4] and KNIME [5] for these data mining techniques. We have used Statlog heart disease dataset [6] for analyzing performance of tools.
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