Hybrid Approaches for Clustering

  • Laxmi Kankanala
  • M. Narasimha Murty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

Applications in various domains often lead to very large and frequently high-dimensional data. Successful algorithms must avoid the curse of dimensionality but at the same time should be computationally efficient. Finding useful patterns in large datasets has attracted considerable interest recently. The primary goal of the paper is to implement an efficient Hybrid Tree based clustering method based on CF-Tree and KD-Tree, and combine the clustering methods with KNN-Classification. The implementation of the algorithm involves many issues like good accuracy, less space and less time. We will evaluate the time and space efficiency, data input order sensitivity, and clustering quality through several experiments.

Keywords

Leaf Node Design Phase Class Number Cluster Feature Binary Search Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Laxmi Kankanala
    • 1
  • M. Narasimha Murty
    • 1
  1. 1.Indian Institute of Science, BangaloreIndia

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