A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic validation approach for clustering
Pattern Recognition Letters
A monothetic clustering method
Pattern Recognition Letters
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
New indices for cluster validity assessment
Pattern Recognition Letters
DIVCLUS-T: A monothetic divisive hierarchical clustering method
Computational Statistics & Data Analysis
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dissimilarity measures and divisive clustering for symbolic multimodal-valued data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Clustering is an explanatory procedure which helps to understand data with complex structure and multivariate relationships, and is a very useful method to extract knowledge and information especially from large datasets. When such datasets are aggregated into categories (as driven by scientific questions underlying the analysis), the resulting observations will perforce be expressed as so-called symbolic data (though symbolic data can occur ''naturally'' in any sized datasets). The focus of this work is to provide a divisive polythetic algorithm to establish clusters for p-dimensional histogram-valued data. In addition, two cluster validity indexes for use in establishing the optimal number of clusters are also developed. Finally, the proposed procedure is applied to a large forestry cover type dataset.