Clustering with Instance and Attribute Level Side Information

Abstract

Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.

Some Indexes for Comparing and Selecting Partitions

“…More general, human beings use perceptions of direction, speed, time and other features of physical/mental objects to process information, e.g., driving and cooking. Perceptions are granular (information granular), which are collections of objects arranged together based on their similarity, functional adjacency and indistinguishability 23,24 , information granulation exhibits different facets of formalism and as such rely on the well established theories of interval and interval calculus, fuzzy sets, rough sets and alike 6,7,8,9,11 . From the mathematical point of view, the fundamental of information granulation is relations on the set of objects, objects can be easily managed by these relations on the set of objects 15,16,17,19 .…”