Constraint-based graph clustering through node sequencing and partitioning

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/3641

Title: Constraint-based graph clustering through node sequencing and partitioning
Author(s): Lai, Wei; Qian, Yu; Zhang, Kang
Abstract: This paper proposes a two-step graph partitioning method to discover constrained clusters with an objective function that follows the well-known min-max clustering principle. Compared with traditional approaches, the proposed method has several advantages. Firstly, the objective function not only follows the theoretical min-max principle but also reflects certain practical requirements. Secondly, a new constraint is introduced and solved to suit more application needs while unconstrained methods can only control the number of produced clusters. Thirdly, the proposed method is general and can be used to solve other practical constraints. The experimental studies on word grouping and result visualization show very encouraging results.
Publication type: Conference paper
Research centre: Swinburne University of Technology. Faculty of Information and Communication Technologies
Source: Paper presented to the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Vol. 3056 (2004), pp. 41-51
Publication year: 2004
Publisher: Springer-Verlag Berlin
Format: pp. 41-51
Publisher URL: http://dx.doi.org/10.1007/b97861
Peer reviewed