Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool

Authors

  • GG Gokilam Department of Computer Science and Engineering, PRIST University, TamilNadu, India
  • K Shanthi Department of Computer Science and Engineering, Principal of Ponnaiyah Ramajayam Polytechnic College, TamilNadu, India

Keywords:

Cluster, Diabetes, Weka, Data Mining

Abstract

Data mining is the process of discover useful information from large datasets. The data mining techniques are used to analyze and evaluate diabetic dataset in the field of bio-medical. One of the most important techniques of data mining is clustering which is used to analyzing data from different perspectives and summarizing into useful information. Clustering is the task of assigning a set of objects into group called clusters. This paper discusses different clustering algorithms like cobweb, DBSCAN, EM, Farthest first, filtered cluster hierarchical cluster, OPTICS, simple Kmeans. The algorithms are used to compare its performance by Time taken to build the clusters, the cluster differentiated by its true positive and true negative values. Our main aim to show the comparison of the different cluster algorithms are evaluated in weka tool (Data mining Tool) and find out which algorithm will be most suitable for the diabetes dataset.

References

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Published

2015-02-28

How to Cite

[1]
G. Gokilam and K. Shanthi, “Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool”, Int. J. Comp. Sci. Eng., vol. 3, no. 2, pp. 1–5, Feb. 2015.

Issue

Vol. 3 No. 2 (2015): IJCSE February Edition

Section

Review Article

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