A simulated annealing algorithm for the clustering problem
Pattern Recognition
Vector quantization and signal compression
Vector quantization and signal compression
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
An Interior Point Algorithm for Minimum Sum-of-Squares Clustering
SIAM Journal on Scientific Computing
Journal of Global Optimization
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
NP-hardness of Euclidean sum-of-squares clustering
Machine Learning
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Differential evolution with competing strategies applied to partitional clustering
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
Hi-index | 0.10 |
The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms.