A Framework for Mining Heterogeneous Dataset
13 Pages Posted: 9 Mar 2018
Date Written: November 15, 2017
Many have explored various clustering strategies to partition heterogeneous datasets with numeric, binary, nominal and ordinal attributes. The clustering algorithms seek to identify similar groups of objects based on the values of their attributes. These algorithms either assume the attributes to be of homogeneous types or are transformed to homogeneous types. In the real world, the dataset are often with heterogeneous nature. If those data are converted it leads to information loss. This paper proposes a hybrid methodology with unified similarity measure to cluster dataset of heterogeneous nature. The proposed hybrid clustering algorithm identifies the similar set of objects with heterogeneous data types without changing its characteristics. The empirical results show that the proposed clustering algorithm yields better results.
Keywords: Distance Measure, Hybrid Clustering, Heterogeneous Data, Fusing Technique
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