ST-DBSCAN: An algorithm for clustering spatial-temp oral data

Birant D., Kut R. A.

DATA & KNOWLEDGE ENGINEERING, cilt.60, sa.1, ss.208-221, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60 Sayı: 1
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.datak.2006.01.013
  • Dergi Adı: DATA & KNOWLEDGE ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.208-221
  • Anahtar Kelimeler: data mining, cluster analysis, spatial-temporal data, cluster visualization, algorithms, DATABASES, DENSITY
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial-temporal data warehouse system designed for storing and clustering a wide range of spatial-temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results. (c) 2006 Elsevier B.V. All rights reserved.