COE: Clustering with Obstacles Entities. A Preliminary Study
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Clustering analysis has been a very active area of research in the data mining community. However, most algorithms have ignored the fact that physical obstacles exist in the real world and could thus affect the result of clustering dramatically. In this paper, we will look at the problem of clustering in the presence of obstacles. We called this problem the COE (Clustering with Obstacles Entities) problem and provide an outline of an algorithm called COE-CLARANS to solve it.
[1] Jiawei Han,et al. Data Mining: Concepts and Techniques , 2000 .
[2] Vipin Kumar,et al. Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.
[3] Paul S. Bradley,et al. Scaling Clustering Algorithms to Large Databases , 1998, KDD.
[4] Tian Zhang,et al. BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.
[5] Jiawei Han,et al. Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.