Analysis of Clustering Algorithms on UCI Repository data set

Deepa Bhargava, Vandana Mohindru, Manish Maan

Abstract


Data mining, the extraction of hidden descriptive information from large database, is a powerful new technology with great potential to
help companies and data analysts focus on the most important information in their data repositories. The data clustering is an important problem
in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. There are various algorithms used to solve this
problem. This paper presents the comparison of the performance analysis of Fuzzy C mean (FCM) clustering algorithm and compares it with
Hard C Mean (HCM) algorithm on Iris flower data set. We measure Time complexity and space Complexity of FCM and HCM at Iris data [1]
set. FCM clustering [2, 3] is a clustering technique which is separated from Hard C Mean that employs hard partitioning. The FCM employs
fuzzy portioning such that a point can belong to all groups with different membership grades between 0 and 1.

 

 

Keywords: Clustering, Data Mining, Fuzzy C mean, Hard C mean.


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DOI: https://doi.org/10.26483/ijarcs.v2i2.396

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