Algorithms for clustering data
Algorithms for clustering data
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
An efficient agglomerative clustering algorithm using a heap
Pattern Recognition
Vector quantization and signal compression
Vector quantization and signal compression
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
Genetic algorithm with deterministic crossover for vector quantization
Pattern Recognition Letters
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Self-Adaptive Genetic Algorithm for Clustering
Journal of Heuristics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the computational complexity of the LBG and PNN algorithms
IEEE Transactions on Image Processing
Fast and memory efficient implementation of the exact PNN
IEEE Transactions on Image Processing
A fast exact GLA based on code vector activity detection
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
On the efficiency of swap-based clustering
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
An incremental-encoding evolutionary algorithm for color reduction in images
Integrated Computer-Aided Engineering
Using genetic algorithm for selection of initial cluster centers for the K-means method
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Comparison of clustering methods: A case study of text-independent speaker modeling
Pattern Recognition Letters
Isolating top-k dense regions with filtration of sparse background
Pattern Recognition Letters
Density-based hierarchical clustering for streaming data
Pattern Recognition Letters
Clustering by analytic functions
Information Sciences: an International Journal
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Agglomerative clustering generates the partition hierarchically by a sequence of merge operations. We propose an alternative to the merge-based approach by removing the clusters iteratively one by one until the desired number of clusters is reached. We apply local optimization strategy by always removing the cluster that increases the distortion the least. Data structures and their update strategies are considered. The proposed algorithm is applied as a crossover method in a genetic algorithm, and compared against the best existing clustering algorithms. The proposed method provides best performance in terms of minimizing intra-cluster variance.