An empirical comparison of Clustering using hierarchical methods and K-means

  title={An empirical comparison of Clustering using hierarchical methods and K-means},
  author={P. Praveen and Bulusu Rama},
  journal={2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB)},
Data clustering is the process of grouping data elements based on some aspects of relationship between the elements in the group Clustering has many applications such as data firmness, data mining pattern recognition machine learning and there are many different clustering methods. This paper examines the K-means method of clustering and how the selection of primary seeding affects the result. Hierarchical algorithms are used as a base line and it is compared to a data set. 

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Showing 1-10 of 13 references

k-mean clustering Algorithm for mixed numeric and categorical data,Data

  • Amer, Ahmad, A Lipika Dey
  • &Knowledge Engineering,Volume63,Issue
  • 2007
Highly Influential
3 Excerpts

Data clusterin : a review

  • A. K. Jain, M. N. Murty, P. J. Flynn
  • Appl . Note
  • 2008

An algorithm for constructing clusters from a distance matrix

  • J. D. Apresjan
  • Mashinnyi perevod : Prikladnaja lingvistika
  • 2007

CHAMELEON : Ahierarchical clustering algorithm using dynamic modeling

  • Jiawei Han Micheline Kamber, Takashi Yamaguchi, +5 authors V. Kumar
  • IEEE Comput .
  • 1999

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