Multi-way Clustering on Relation Graphs

A number of real-world domains such as social networks and e-commerce involve heterogeneous data that describes relations between multiple classes of entities. Understanding the natural structure of this type of heterogeneous relational data is essential both for exploratory analysis and for performing various predictive modeling tasks. In this paper, we propose a principled multi-way clustering framework for relational data, wherein different types of entities are simultaneously clustered based not only on their intrinsic attribute values, but also on the multiple relations between the entities. To achieve this, we introduce a relation graph model that describes all the known relations between the different entity classes, in which each relation between a given set of entity classes is represented in the form of multi-modal tensor over an appropriate domain. Our multi-way clustering formulation is driven by the objective of capturing the maximal “information” in the original re...

• Type: Conference
• Year: 2007
• Where: SDM
• Authors: Arindam Banerjee, Sugato Basu, Srujana Merugu
• Keywords: Data Mining, Multi-way Clustering, Principled Multi-way Clustering, Relation Graph, SDM 2007