Publication Type:

Conference Paper

Source:

Proceedings of the Third International Symposium on Women in Computing and Informatics, ACM, New York, NY, USA (2015)

ISBN:

9781450333610

URL:

http://doi.acm.org/10.1145/2791405.2791437

Keywords:

Clustering, Discretization, mixed data, Pairwise distance, rule mining

Abstract:

The field of data mining is concerned with finding interesting patterns from an unstructured data. A simple, popular as well as an efficient clustering technique for data analysis is k-means. But classical k-means algorithm can only be applied to numerical data where k is a user given value. But the data generated from a wide variety of domains are of mixed form and it is effortful to trust on a user given value for k. So our objective is to effectively use an association rule mining algorithm which can automatically compute the number of clusters and a pairwise distance measure for calculating the distance in mixed data. We have done experimentations with real mixed data taken from the UCI repository.

Cite this Research Publication

H. Haripriya, Amrutha, S., Veena, R., and Prof. Prema Nedungadi, “Integrating Apriori with Paired K-means for Cluster Fixed Mixed Data”, in Proceedings of the Third International Symposium on Women in Computing and Informatics, New York, NY, USA, 2015.

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