A new eigenvector selection strategy applied to develop spectral clustering
| Autor: | F. Torkamani Azar, M. Hosseini |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Clustering high-dimensional data
Fuzzy clustering Correlation clustering MathematicsofComputing_NUMERICALANALYSIS 010103 numerical & computational mathematics 02 engineering and technology computer.software_genre 01 natural sciences Artificial Intelligence CURE data clustering algorithm 0202 electrical engineering electronic engineering information engineering 0101 mathematics Cluster analysis Mathematics business.industry Applied Mathematics Pattern recognition Spectral clustering Computer Science Applications ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture Signal Processing Canopy clustering algorithm Affinity propagation 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer Software Information Systems |
| Zdroj: | Multidimensional Systems and Signal Processing. 28:1227-1248 |
| ISSN: | 1573-0824 0923-6082 |
| DOI: | 10.1007/s11045-016-0391-6 |
| Popis: | Spectral methods are strong tools that can be used for extraction of the data’s structure based on eigenvectors of constructed affinity matrices. In this paper, we aim to propose some new measurement functions to evaluate the ability of each eigenvector of affinity matrix in data clustering. In the proposed strategy, each eigenvector’s elements are clustered by traditional fuzzy c-means algorithm and then informative eigenvectors selection is performed by optimization of an objective function which defined based on three criterions. These criterions are the compactness of clusters, distance between clusters and stability of clustering to evaluate each eigenvector based on considering the structure of clusters which placed on. Finally, Lagrange multipliers method is used to minimize the proposed objective function and extract the most informative eigenvectors. To indicate the merits of our algorithm, we consider UCI Machine Learning Repository databases, COIL20, YALE-B and PicasaWeb as benchmark data sets. Our simulation’s results confirm the superior performance of the proposed strategy in developing spectral clustering compared to conventional clustering methods and recent eigenvector selection based algorithms. |
| Databáze: | OpenAIRE |
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