Semi-supervised clustering and local scale learning algorithm

Abstract

We propose a new semi-supervised relational clustering approach, called Semi-Supervised relational clustering with local scaling parameter (SS-LSL). The proposed algorithm learns a cluster dependent Gaussian kernel while finding compact clusters. SS-LSL uses side-information in the form of a small set of constraints on which instances should or should not reside in the same cluster. The proposed algorithm uses only the pairwise relation between the feature vectors. This makes it applicable when similar objects cannot be represented by a single prototype. Using synthetic and real data sets, we show that SS-LSL outperforms several other algorithms.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

“…(47), the weight w ∈ (0, 1) provides a way of specifying the relative importance of the "Should-Link" and "Should not-Link" constraints compared to the sum of inter-cluster distances. In [48], the authors recommend fixing it as the ratio of the number of constraints to the total number of points.…”