Towards Unsupervised and Consistent High Dimensional Data Clustering

Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
The boosted demand for immense information, the enhanced data acquisition and so do the size and number of dimensions of data is a big challenge for the data mining algorithms. Clustering exercise to collect the data with same characteristics together, for better performance of knowledge based systems. High dimensional and large size data results in declined performance of existing clustering algorithms. PROCLUS is an efficient high dimensional clustering algorithm; consist of significant issues like inconsistency in results and expert supervised subspaces.

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