An optimal method for data clustering. Issue 2 (February 2016)

• Record Type: Journal Article
• Authors: Xie, Linsen; Lu, Chengbo; Mei, Ying; Du, Hong; Man, Zhihong
• Is Part Of: Neural computing & applications. Volume 27:Issue 2(2016)
• Page Start: 283
• Page End: 289
• Publication Date: 2016-02
• Subjects: Clustering -- Extreme learning machine -- Feature space -- Graph Laplacian; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-014-1818-3
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract An algorithm for optimizing data clustering in feature space is studied in this work. Using graph Laplacian and extreme learning machine (ELM) mapping technique, we develop an optimal weight matrixW for feature mapping. This work explicitly performs a mapping of the original data for clustering into an optimal feature space, which can further increase the separability of original data in the feature space, and the patterns points in same cluster are still closely clustered. Our method, which can be easily implemented, gets better clustering results than some popular clustering algorithms, likek -means on the original data, kernel clustering method, spectral clustering method, and ELMk -means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database).