An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
ACM Transactions on Knowledge Discovery from Data (TKDD)
Robust path-based spectral clustering
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using the clustering coefficient to guide a genetic-based communities finding algorithm
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Computer Science Review
A genetic graph-based clustering approach to biomedical summarization
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
| Hi-index | 0.00 |
The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.