MK-Prototypes: A Novel Algorithm for Clustering Mixed Type Data

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Provided by: International Journal of Modern Engineering Research (IJMER)
Topic: Data Management
Format: PDF
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.
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