A Heuristic Automatic Clustering Method Based on Hierarchical Clustering
2014
We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. The proposed method achieves these results with minimal user input, of which none pertains to a number of clusters. Our method's effectiveness in clustering, including its ability to produce valid results on data sets presenting nested or interlocking shapes, is demonstrated and compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided, and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.
Keywords:
- Single-linkage clustering
- Data mining
- Correlation clustering
- Machine learning
- k-medians clustering
- Computer science
- Determining the number of clusters in a data set
- Cluster analysis
- Canopy clustering algorithm
- CURE data clustering algorithm
- Brown clustering
- Artificial intelligence
- Pattern recognition
- Constrained clustering
- Fuzzy clustering
- Hierarchical clustering
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