Local Density Differ Spatial Clustering in Data Mining

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Clustering in data mining is a discovery process that groups a set of data objects so that the inter-cluster similarity is minimized and intra-cluster similarity is maximized. In presence of noise and outlier in high dimensional data base it is a difficult task to find out the clusters of different shapes, sizes and differ in density. Density based clustering algorithms like DBSCAN finds the clusters based on density property but still within the same cluster the major density difference may exist due to the only minimum point value.

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