- Title
- kNN-MST-Agglomerative: a fast and scalable graph-based data clustering approach on GPU
- Creator
- Arefin, Ahmed Shamsul; Riveros, Carlos; Berretta, Regina; Moscato, Pablo
- Relation
- 7th International Conference on Computer Science & Education (ICCSE 2012). Proceedings of 2012 7th International Conference on Computer Science & Education (ICCSE 2012) (Melbourne 14-17 July, 2012) p. 585-590
- Publisher Link
- http://dx.doi.org/10.1109/ICCSE.2012.6295143
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2012
- Description
- Data clustering is a distinctive method for analyzing complex networks in terms of functional relationships of the comprising elements. A number of graph-based algorithms have been proposed so far to tackle the complexity of the problem and many of them are based on the representation of data in the form of a minimum spanning tree (MST). In this work, we propose a graph-based agglomerative clustering method that is based the k-Nearest Neighbor (kNN) graphs and the Boruvka's-MST Algorithm, (termed as, kNN-MST-Agglomerative). The proposed method is inherently parallel and in addition it is applicable to a wide class of practical problems involving large datasets. We demonstrate the performance of our method on a set of real-world biological networks constructed from a renowned breast cancer study.
- Subject
- data clustering; datasets; algorithms
- Identifier
- http://hdl.handle.net/1959.13/1057514
- Identifier
- uon:16196
- Identifier
- ISBN:9781467302425
- Language
- eng
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