Algorithms for clustering data
Algorithms for clustering data
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Clustering Algorithms
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
BANG-Clustering: A Novel Grid-Clustering Algorithm for Huge Data Sets
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Clustering categorical data: an approach based on dynamical systems
The VLDB Journal — The International Journal on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A genetic rule-based data clustering toolkit
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Putting more genetics into genetic algorithms
Evolutionary Computation
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A comprehensive validity index for clustering
Intelligent Data Analysis
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
Hybrid ensemble approach for classification
Applied Intelligence
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
Various methods have been proposed to utilize Genetic Algorithms (GA) in handling the clustering problem. GA work on encoded strings, namely chromosomes, and the representation of different clusters as a linear structure is an important issue about the usage of GA in this domain. In this paper, we present a novel encoding scheme that uses links to identify clusters in a partition. Particularly, we restrict the links so that objects to be clustered form a linear pseudo-graph. A one-to-one mapping is thus achieved between the genotype and phenotype spaces. The other feature of the proposed approach is the use of multiple objectives in the process. One of the two objectives we use is to minimize the Total Within Cluster Variation (TWCV), identical to the one used by other k-means clustering approaches. However, unlike other k-means methods, number of clusters is not required specified in advance. Combined with a second objective, minimizing the number of clusters in a partition, our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single GA run. The performance of the proposed approach has been tested using two well-known data sets, namely Iris Data and Ruspini Data. The obtained results are compared with the output of the classical Group Number Encoding and it has been observed that a clear improvement has been achieved with the new representation.