Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes

  • Bidyut Kr. Patra
  • Ollikainen Ville
  • Raimo Launonen
  • Sukumar Nandi
  • Korra Sathya Babu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


Clustering has been recognized as one of the important tasks in data mining. One important class of clustering is distance based method. To reduce the computational and storage burden of the classical clustering methods, many distance based hybrid clustering methods have been proposed. However, these methods are not suitable for cluster analysis in dynamic environment where underlying data distribution and subsequently clustering structures change over time. In this paper, we propose a distance based incremental clustering method, which can find arbitrary shaped clusters in fast changing dynamic scenarios. Our proposed method is based on recently proposed al-SL method, which can successfully be applied to large static datasets. In the incremental version of the al-SL (termed as IncrementalSL), we exploit important characteristics of al-SL method to handle frequent updates of patterns to the given dataset. The IncrementalSL method can produce exactly same clustering results as produced by the al-SL method. To show the effectiveness of the IncrementalSL in dynamically changing database, we experimented with one synthetic and one real world datasets.


Incremental clustering arbitrary shaped clusters large datasets 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bidyut Kr. Patra
    • 1
    • 3
  • Ollikainen Ville
    • 1
  • Raimo Launonen
    • 1
  • Sukumar Nandi
    • 2
  • Korra Sathya Babu
    • 3
  1. 1.VTT Technical Research Centre of FinlandEspooFinland
  2. 2.Indian Institute of Technology GuwahatiGuwahatiIndia
  3. 3.RourkelaNational Institute of Technology RourkelaOdishaIndia

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