How many clusters are best?—an experiment
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering gene expression patterns
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Semantic Clustering of Index Terms
Journal of the ACM (JACM)
An Analysis of Some Graph Theoretical Cluster Techniques
Journal of the ACM (JACM)
Machine Learning
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Clustering with a minimum spanning tree of scale-free-like structure
Pattern Recognition Letters
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A Comparison of the Stability Characteristics of Some Graph Theoretic Clustering Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
Dense subgraph mining with a mixed graph model
Pattern Recognition Letters
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Clustering is an important tool to explore the hidden structure of large databases. There are several algorithms based on different approaches (hierarchical, partitional, density-based, model-based, etc.). Most of these algorithms have some discrepancies, e.g. they are not able to detect clusters with convex shapes, the number of the clusters should be a priori known, they suffer from numerical problems, like sensitiveness to the initialization, etc. In this paper we introduce a new clustering algorithm based on the sinergistic combination of the hierarchial and graph theoretic minimal spanning tree based clustering and the partitional Gaussian mixture model-based clustering algorithms. The aim of this hybridization is to increase the robustness and consistency of the clustering results and to decrease the number of the heuristically defined parameters of these algorithms to decrease the influence of the user on the clustering results. As the examples used for the illustration of the operation of the new algorithm will show, the proposed algorithm can detect clusters from data with arbitrary shape and does not suffer from the numerical problems of the Gaussian mixture based clustering algorithms.