Clustering In A High-Dimensional Space Using Hypergraph Models ∗


Clustering of data in a large dimension space is of a great interest in many data mining applications. Most of the traditional algorithms such as K-means or AutoClass fail to produce meaningful clusters in such data sets even when they are used with well known dimensionality reduction techniques such as Principal Component Analysis and Latent Semantic… (More)

9 Figures and Tables



Citations per Year

80 Citations

Semantic Scholar estimates that this publication has 80 citations based on the available data.

See our FAQ for additional information.

  • Presentations referencing similar topics