Semi-Supervised Clustering with Limited Background Knowledge

Sugato Basu

In many machine learning domains, there is a large supply of unlabeled data but limited labeled data, which can be expensive to generate. Consequently, semi-supervised learning, learning from a combination of both labeled and unlabeled data, has become a topic of significant recent interest. Our research focus is on semi-supervised clustering, which uses a small amount of supervised data in the form of class labels or pairwise constraints on some examples to aid unsupervised clustering. Semi-supervised clustering can be either constraint-based, i.e., changes are made to the clustering objective to satisfy user-specified labels/constraints, or metricbased, i.e., the clustering distortion measure is trained to satisfy the given labels/constraints. Our main goal in this thesis is to study constraint-based semi-supervised clustering algorithms, integrate them with metric-based approaches, characterize some of their properties and empirically validate our algorithms on different domains, e.g., text processing and bioinformatics.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.