Sciweavers

Share
ICML
2005
IEEE

Robust one-class clustering using hybrid global and local search

12 years 6 months ago
Robust one-class clustering using hybrid global and local search
Unsupervised learning methods often involve summarizing the data using a small number of parameters. In certain domains, only a small subset of the available data is relevant for the problem. One-Class Classification or One-Class Clustering attempts to find a useful subset by locating a dense region in the data. In particular, a recently proposed algorithm called One-Class Information Ball (OC-IB) shows the advantage of modeling a small set of highly coherent points as opposed to pruning outliers. We present several modifications to OC-IB and integrate it with a global search that results in several improvements such as deterministic results, optimality guarantees, control over cluster size and extension to other cost functions. Empirical studies yield significantly better results on various real and artificial data.
Gunjan Gupta, Joydeep Ghosh
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Gunjan Gupta, Joydeep Ghosh
Comments (0)
books