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|Title||EOPTICS “Enhancement Ordering Points to Identify the Clustering Structure”|
Grouping a set of physical or abstract objects into classes of similar objects is a process of clustering. Clustering is very important technique in statistical data analysis. Among the clustering methods, density-based methods are critical because of their ability to recognize clusters with arbitrarily shape. In particular, OPTICS density-based method is an improvement upon DBSCAN. It addresses the major DBSCAN's weakness, which is the problem of detecting clusters in data of varying density. OPTICS defines the core distance which is the shortest distance from the core that contains the minimum number of points. Those points within the radius of the core distance may contain points far from the core than all the other points located within the same core distance. This algorithm computes the mean distance among the points within the core distance and the core itself and the resulting distance is considered as the new core distance.
|Published in||International Journal of Computer Applications|
|Series||Volume: 40, Number: 17|
|Publisher||International Journal of Computer Applications, 244 5 th Avenue,# 1526, New York, NY 10001, USA India|
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