Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes

Bidyut Patra, Ville Ollikainen, Raimo Launonen, Sukumar Nandi, Korra Sathya Babu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

5 Citations (Scopus)

Abstract

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
Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence
Subtitle of host publicationPReMI 2013
PublisherSpringer
Pages229-236
ISBN (Electronic)978-3-642-45062-4
ISBN (Print)978-3-642-45061-7
DOIs
Publication statusPublished - 2013
MoE publication typeNot Eligible
Event5th International Conference, PReMI 2013 - Kolkata, India
Duration: 10 Dec 201314 Dec 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8251
ISSN (Print)0302-9743

Conference

Conference5th International Conference, PReMI 2013
Abbreviated titlePReMI 2013
CountryIndia
CityKolkata
Period10/12/1314/12/13

Fingerprint

Cluster analysis
Data mining

Keywords

  • Incremental clustering
  • arbitrary shaped clusters
  • large datasets

Cite this

Patra, B., Ollikainen, V., Launonen, R., Nandi, S., & Sathya Babu, K. (2013). Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes. In Pattern Recognition and Machine Intelligence : PReMI 2013 (pp. 229-236). Springer. Lecture Notes in Computer Science, Vol.. 8251 https://doi.org/10.1007/978-3-642-45062-4_31
Patra, Bidyut ; Ollikainen, Ville ; Launonen, Raimo ; Nandi, Sukumar ; Sathya Babu, Korra. / Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes. Pattern Recognition and Machine Intelligence : PReMI 2013. Springer, 2013. pp. 229-236 (Lecture Notes in Computer Science, Vol. 8251).
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abstract = "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",
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Patra, B, Ollikainen, V, Launonen, R, Nandi, S & Sathya Babu, K 2013, Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes. in Pattern Recognition and Machine Intelligence : PReMI 2013. Springer, Lecture Notes in Computer Science, vol. 8251, pp. 229-236, 5th International Conference, PReMI 2013, Kolkata, India, 10/12/13. https://doi.org/10.1007/978-3-642-45062-4_31

Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes. / Patra, Bidyut; Ollikainen, Ville; Launonen, Raimo; Nandi, Sukumar; Sathya Babu, Korra.

Pattern Recognition and Machine Intelligence : PReMI 2013. Springer, 2013. p. 229-236 (Lecture Notes in Computer Science, Vol. 8251).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Patra B, Ollikainen V, Launonen R, Nandi S, Sathya Babu K. Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes. In Pattern Recognition and Machine Intelligence : PReMI 2013. Springer. 2013. p. 229-236. (Lecture Notes in Computer Science, Vol. 8251). https://doi.org/10.1007/978-3-642-45062-4_31