A monothetic clustering method
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Editorial: Special Issue on Statistical Algorithms and Software
Computational Statistics & Data Analysis
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points
Pattern Recognition Letters
GAP: A graphical environment for matrix visualization and cluster analysis
Computational Statistics & Data Analysis
Using a new relational concept to improve the clustering performance of search engines
Information Processing and Management: an International Journal
A polythetic clustering process and cluster validity indexes for histogram-valued objects
Computational Statistics & Data Analysis
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DIVCLUS-T is a divisive hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. It is designed for either numerical or categorical data. Like the Ward agglomerative hierarchical clustering algorithm and the k-means partitioning algorithm, it is based on the minimization of the inertia criterion. However, unlike Ward and k-means, it provides a simple and natural interpretation of the clusters. The price paid by construction in terms of inertia by DIVCLUS-T for this additional interpretation is studied by applying the three algorithms on six databases from the UCI Machine Learning repository.