K-Mean Clustering Algorithm Approach for Data Mining of Heterogeneous Data

  • Monika Kalra
  • Niranjan Lal
  • Samimul Qamar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


The increasing rate of heterogeneous data gives us new terminology for data analysis and data extraction. With a view toward analysis of heterogeneous sources of data, we consider the challenging task of developing exploratory analytical techniques to explore clustering techniques on heterogeneous data consist of heterogeneous domains such as categorical, numerical, and binary or combination of all these data. In our paper, we proposed a framework for analyzing and data mining of heterogeneous data from a multiple heterogeneous data sources. Clustering algorithms recognize only homogeneous attributes value. However, data in the every field occurs in heterogeneous forms, which if we convert data heterogeneous to homogeneous form can loss of information. In this paper, we applied the K-Mean clustering algorithm on real life heterogeneous datasets and analyses the result in the form of clusters.


Heterogeneous data Clustering K-Mean 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Mody University of Science and TechnologyLakshmangarh, SikarIndia
  2. 2.Suresh Gyan Vihar UniversityJaipurIndia
  3. 3.King Khalid UniversityAbhaKingdom of Saudi Arabia

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