Publication Type : Conference Paper
Thematic Areas : Learning-Technologies
Publisher : Proceedings of the Third International Symposium on Women in Computing and Informatics, ACM, New York, NY, USA.
Source : Proceedings of the Third International Symposium on Women in Computing and Informatics, ACM, New York, NY, USA (2015)
ISBN : 9781450333610
Keywords : Clustering, Discretization, mixed data, Pairwise distance, rule mining
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Technologies & Education (AmritaCREATE), Amrita Center For Research in Analytics
Department : Computer Science
Year : 2015
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
