Algorithms for clustering data
Algorithms for clustering data
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Short communication: Optimising k-means clustering results with standard software packages
Computational Statistics & Data Analysis
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High-dimensional fuzzy clustering may converge to a local optimum that is significantly inferior to the global optimal partition. In this paper, a two-stage fuzzy clustering method is proposed. In the first stage, clustering is applied on the compact data that is obtained by dimensionality reduction from the full-dimensional data. The optimal partition identified from the compact data is then used as the initial partition in the second stage clustering based on full-dimensional data, thus effectively reduces the possibility of local optimum. It is found that the proposed two-stage clustering method can generally avoid local optimum without computation overhead. The proposed method has been applied to identify optimal day groups for traffic profiling using operational traffic data. The identified day groups are found to be intuitively reasonable and meaningful.