In this paper we present a new clustering algorithm which extends the traditional batch k-means enabling the introduction of domain knowledge in the form of Must, Cannot, May and May-Not rules between the data points. Besides, we have applied the presented method to the task of avoiding bias in clustering. Evaluation carried out in standard collections showed considerable improvements in effect...

Abstract Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation. In this work, we first propose a novel spectral-based algorithm that seeks to represent each point sparse convex combination of few nearby points. We then extend the constrained active learning framework. Our motivation ...

We propose two Euclidean minimum spanning tree based clustering algorithms — one a k-constrained, and the other an unconstrained algorithm. Our k-constrained clustering algorithm produces a k-partition of a set of points for any given k. The algorithm constructs a minimum spanning tree of a set of representative points and removes edges that satisfy a predefined criterion. The process is repeat...

Constrained clustering is an important task in Data Mining. In the last ten years, many works have been done to extend classical clustering algorithms to handle user-defined constraints, but they are in general limited to one kind of constraints. In our previous work (Dao et al., 2013a), we have proposed a declarative and general framework, based on Constraint Programming, which enables to desi...

In knowledge bases or information extraction results, differently expressed relations can be semantically similar (e.g., (X, wrote, Y) and (X, ’s written work, Y)). Therefore, grouping semantically similar relations into clusters would facilitate and improve many applications, including knowledge base completion, information extraction, information retrieval, and more. This paper formulates rel...

Witnessing the tremendous development of machine learning technology, emerging applications impose challenges using domain knowledge to improve accuracy clustering provided that suffers a compromising rate despite its advantage fast procession. In this paper, we model (i.e., background or side information), respecting some as must-link and cannot-link sets, for sake collaborating with k-means b...