Discriminative Approach to Semi-Supervised Clustering release_sh7f7yvjn5gaxazp2gdny433vq

by Marek´smieja Marek´smieja

Released as a article-journal .

Abstract

We consider a semi-supervised clustering problem, where selected pairs of data points are labeled by an expert as must-links or cannot-links. Basically, must-link constraints indicate that two points should be grouped together, while those with cannot-link constraints should be grouped separately. We present a clustering algorithm, which creates a partition consistent with pairwise constraints by maximizing the probability of correct assignments. Moreover, unlabeled data are used by maximizing their prediction confidence. Preliminary experimental studies show that the proposed method gives accurate results on sample data sets. Moreover, its kernelization allows to discover clustering patterns of arbitrary shapes.
In text/plain format

Archived Files and Locations

application/pdf  222.9 kB
file_cy45rr54drchln4tpu3bvklija
www.thinkmind.org (web)
web.archive.org (webarchive)
Read Archived PDF
Archived
Type  article-journal
Stage   unknown
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 03bd154d-4adc-4258-90cc-73447d9418b1
API URL: JSON