| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Dept Matemat Aplicada & Estat, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 391, n. 23, p. 6174-6183, DEC 1 2012. |
| Web of Science Citations: | 8 |
| Abstract | |
This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 05/00587-5 - Mesh (graph) modeling and techniques of pattern recognition: structure, dynamics and applications |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 10/19440-2 - Characterization, analysis, simulation and classification of complex networks |
| Grantee: | Francisco Aparecido Rodrigues |
| Support Opportunities: | Regular Research Grants |