A Survey on Mining Actionable Clusters from High Dimensional Datasets

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
The datasets which are in the form of object-attribute-time format is referred to as Three-Dimensional (3D) data sets. Clustering these Three Dimensional (3D) data sets is a difficult task. So the subspace clustering method is applied to cluster the Three-Dimensional (3D) data sets. But finding the subspaces in the these Three-Dimensional (3D) dataset which is changing over time is really a difficult task. Sometimes this subspace clustering on Three-Dimensional (3D) data sets may produce the large number of arbitrary and spurious clusters. To cluster these three-dimensional many algorithms like MASC,TRICLUSTER,MIC,GS-Search and FCC is used now-a-days. But these algorithms allow the users to select the preferred objects as centroids.

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