An Efficient Density Conscious Subspace Clustering Method Using Top-Down and Bottom-Up Strategies

Provided by: International Journal of Computing Science and Information Technology (IJCSIT)
Topic: Big Data
Format: PDF
Clustering high dimensional data is an emerging research field. Most clustering technique use distance measures to build clusters. In high dimensional spaces, traditional clustering algorithms suffers from a problem called \"Curse of dimensionality\". Subspace clustering groups similar objects embedded in subspace of full space. Recent approaches attempt to find clusters embedded in subspace of high dimensional data. Most of the previous subspace clustering works discovers subspace clusters, by regarding the clusters as regions of higher densities.

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