Merging Modal Clusters via Significance Assessment
Abstract
To deal with superfluous clusters and to reduce the number of clusters as often desired in practice, a modal cluster merging procedure is proposed. Based on some new properties established in this paper for Morse functions, the procedure merges clusters in a sequential manner without causing unnecessary density distortion. Each cluster is evaluated for its significance relative to the other clusters, using the Kullback-Leibler divergence or its log-likelihood approximation, by truncating the density for the cluster at an appropriate level. The least significant cluster is then merged into one of its adjacent clusters, using the novel concept of cluster adjacency defined in this paper. The resulting hierarchical clustering tree is useful for determining the number of clusters, as may be preferred by a specific user or in a general, meaningful manner. Numerical studies show that the new procedure deals well with difficult clustering problems and often produces intuitively appealing and numerically more accurate clustering results, as compared with several other popular clustering methods in the literature.
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BibTeX
@misc{wang2026merging,
title = {{Merging Modal Clusters via Significance Assessment}},
author = {Yong Wang and Shengwei Hu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01958},
eprint = {2609.01958},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01958},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Merging Modal Clusters via Significance Assessment
AU - Yong Wang
AU - Shengwei Hu
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.01958
UR - https://arxiv.org/abs/2609.01958
AB - To deal with superfluous clusters and to reduce the number of clusters as often desired in practice, a modal cluster merging procedure is proposed. Based on some new properties established in this paper for Morse functions, the procedure merges clusters in a sequential manner without causing unnecessary density distortion. Each cluster is evaluated for its significance relative to the other clusters, using the Kullback-Leibler divergence or its log-likelihood approximation, by truncating the density for the cluster at an appropriate level. The least significant cluster is then merged into one of its adjacent clusters, using the novel concept of cluster adjacency defined in this paper. The resulting hierarchical clustering tree is useful for determining the number of clusters, as may be preferred by a specific user or in a general, meaningful manner. Numerical studies show that the new procedure deals well with difficult clustering problems and often produces intuitively appealing and numerically more accurate clustering results, as compared with several other popular clustering methods in the literature.
ER -