Comparative Study of Techniques and Issues in Data Clustering

  • Parneet KaurEmail author
  • Kamaljit Kaur
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 8)


Data mining refers to the extraction of obscured prognostic details of data from large databases. The extracted information is visualized in the form of charts, graph, tables and other graphical forms. Clustering is an unsupervised approach under data mining which groups together data points on the basis of similarity and separate them from dissimilar objects. Many clustering algorithms such as algorithm for mining clusters with arbitrary shapes (CLASP), Density peaks (DP) and k-means are proposed by different researchers in different areas to enhance clustering technique. The limitation addressed by one clustering technique may get resolved by another technique. In this review paper our main objective is to do comparative study of clustering algorithms and issues arising during clustering process are also identified.


Data mining Database Clustering k-means clustering Outliers 


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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering and TechnologyGuru Nanak Dev UniversityAmritsarIndia

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