MK-Prototypes: A Novel Algorithm for Clustering Mixed Type Data
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 4
Abstract
Clustering mixed type data is one of the major research topics in the area of data mining. In this paper, a new algorithm for clustering mixed type data is proposed where the concept of distribution centroid is used to represent the prototype of categorical variables in a cluster which is then combined with the mean to represent the prototype of clusters with mixed type variables. In the method, data is observed from different views and the variables are grouped into different views. Those instances that can be viewed differently from different viewpoints can be defined as multiview data. During clustering process the differences among views are ignored in usual cases. Here, both views and variables weights are computed simultaneously. The view weight is used to determine the closeness or density of view and variable weight is used to identify the significance of each variable. With the intention of determining the cluster of objects both these weights are used in the distance function. In the proposed method, enhancement to the k-prototypes is done so that it automatically computes both view and variable weights. The proposed algorithm MK-Prototypes algorithm is compared with two other clustering algorithms.
Authors and Affiliations
N. Aparna1 , M. Kalaiarasu2
Keywords
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- EP ID EP115895
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