Constrained Clustering with Seeds and Term Weighting Scheme
Uraiwan Buatoom, Waree Kongprawechnon, Thanaruk Theeramunkong · 2018
While traditional unsupervised learning is blind and the performance relies on the choice of initial seeds. The idea of constrained clustering can use a small number of labeled instances to partly guide a large number of unlabeled instances. It focuses on a set of predefined classes with an aim is to increase the performance of supervised and unsupervised learning using constraints. This paper proposes a new idea of semi-supervised learning based on particularly seeded constrained clustering, where the clustering guidance comes from the statistics of a small set of labeled data. In contrast with existing approaches in seeded K-Means where the labeled instances are specified. However, the proposed work investigates how weighting obtained from a training set affects the seeded-clustering results. Experimental results are demonstrated on three groups of term-weighting statistics; in-collection, intra-class, and inter-class based on frequencies/distributions and an ambiguity class pass entropy value. Text datasets is studied in our experiment. The result also depicts that the term weighting scheme is a potential mean to control/guide the initial and clustering process over a standard normal term weighting scheme.