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Clustering Mixed Data Set by Fuzzy Set Partitioning

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Nipjyoti Sarma, Arindam Saha, Adarsh Pradhan
10.5120/ijca2016910305

Nipjyoti Sarma, Arindam Saha and Adarsh Pradhan. Clustering Mixed Data Set by Fuzzy Set Partitioning. International Journal of Computer Applications 144(6):8-12, June 2016. BibTeX

@article{10.5120/ijca2016910305,
	author = {Nipjyoti Sarma and Arindam Saha and Adarsh Pradhan},
	title = {Clustering Mixed Data Set by Fuzzy Set Partitioning},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {6},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {8-12},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume144/number6/25181-2016910305},
	doi = {10.5120/ijca2016910305},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

K mean clustering is a very popular clustering algorithm for clustering numerical data. . It is popular due to its simplicity of understanding and linear algorithmic complexity measure. But it has the serious limitation of clustering numerical only data. Therefore several researchers tried to improve the k mean algorithm to cluster not only numerical but also categorical dataset. In this work an effort have been made to put forward a proposed FCV mean algorithm which is a modified version of the traditional k-mean algorithm and is able to cluster objects having mixed type attributes i.e. numerical and categorical. For categorical data fuzzy set similarity is used and for numerical data differences from maximum dissimilarity is used. Experiment shows that the mixed data are highly clustered with high accuracy compared to other approach in literature.

References

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Keywords

fuzzy set, Centroid vector, dissimilarity, categorical, numerical.