Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Introduction to Clustering Large and High-Dimensional Data
Introduction to Clustering Large and High-Dimensional Data
A Unified Continuous Optimization Framework for Center-Based Clustering Methods
The Journal of Machine Learning Research
An efficient k'-means clustering algorithm
Pattern Recognition Letters
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
A review of recent advances in global optimization
Journal of Global Optimization
A toolbox for K-centroids cluster analysis
Computational Statistics & Data Analysis
Pattern recognition to forecast seismic time series
Expert Systems with Applications: An International Journal
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance based earthquake evaluation of reinforced concrete buildings using design of experiments
Expert Systems with Applications: An International Journal
Simultaneous seismic wave clustering and registration
Computers & Geosciences
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In this paper a new fast partitioning algorithm able to find either a globally optimal partition or a locally optimal partition of the set A@?R^n close to the global one is proposed. The performance of the algorithm in terms of CPU time shows significant improvement in comparison with other incremental algorithms. Since optimal partitions with 2, 3,... clusters are determined successively in the algorithm, it is possible to calculate corresponding clustering validity indexes for every number of clusters in a partition. In that way the algorithm also proposes the appropriate number of clusters in a partition. The algorithm is illustrated and tested on several synthetic and seismic activity data from a wider area of the Republic of Croatia in order to locate the most intense seismic activity in the observed area.