An optimal method for data clustering
2016
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 matrix W 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, like k-means on the original data, kernel clustering method, spectral clustering method, and ELM k-means on data include three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, and Wine database).
Keywords:
- Fuzzy clustering
- Data stream clustering
- Machine learning
- Correlation clustering
- Cluster analysis
- k-medians clustering
- CURE data clustering algorithm
- Canopy clustering algorithm
- FLAME clustering
- Artificial intelligence
- Pattern recognition
- Computer science
- Clustering high-dimensional data
- Constrained clustering
- Single-linkage clustering
- Data mining
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