The week's pick
Reseach Article
Analysis of Different Data Mining Tools using Classification, Clustering and Association Rule Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 93 - Number 8 |
| Year of Publication: 2014 |
| Authors: Pritam H. Patil, Suvarna Thube, Bhakti Ratnaparkhi, K. Rajeswari |
10.5120/16238-5766
|
Pritam H. Patil, Suvarna Thube, Bhakti Ratnaparkhi, K. Rajeswari . Analysis of Different Data Mining Tools using Classification, Clustering and Association Rule Mining. International Journal of Computer Applications. 93, 8 ( May 2014), 35-39. DOI=10.5120/16238-5766
Abstract
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.
References
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- http://www. cs. waikato. ac. nz/ml/weka
- http://www. solver. com/xlminer-data-mining
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- http://repository. seasr. org/Datasets/UCI/arff
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- http://www. knime. org
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- P. Isakki alias Devi, S. P. Rajagopalan "Analysis of Customer Behavior using Clustering and Association Rules", International Journal of Computer Applications (0975 – 8887) Volume 43– No. 23, April 2012
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