Implementing agglomerative hierarchic clustering algorithms for use in document retrieval
Information Processing and Management: an International Journal
Algorithms for clustering data
Algorithms for clustering data
Vector quantization and signal compression
Vector quantization and signal compression
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Stochastic K-means algorithm for vector quantization
Pattern Recognition Letters
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Encyclopedia Of Data Warehousing And Mining
Encyclopedia Of Data Warehousing And Mining
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Kernel based automatic clustering using modified particle swarm optimization algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A survey of kernel and spectral methods for clustering
Pattern Recognition
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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As an important technique for data analysis, clustering has been employed in many applications such as image segmentation, document clustering and vector quantization. Divisive clustering, which is a branch of hierarchical clustering, has been studied and widely used due to its computational efficiency. Generally, which cluster should be split and how to split the selected cluster are two major principles that should be taken into account when a divisive clustering algorithm is used. However, one disadvantage of the divisive clustering is its degraded performance compared to the partitional clustering, thus making it hard to achieve a good trade-off between computational time and clustering performance. To tackle this problem, we propose a novel divisive clustering algorithm by integrating an improved discrete particle swarm optimizer into a divisive clustering framework. Experiments on several synthetic data sets, real-world data sets and two real-world applications (document clustering and vector quantization) show some promising results. Firstly, the proposed algorithm performs better or at least comparable to the other representative clustering algorithms in terms of clustering quality and robustness. Secondly, the proposed algorithm runs much faster than the other competing algorithms on all the benchmark sets. At last, the good time-quality trade-off is still achievable when the size of the problem instance is increased.