A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm

Clustering is a field of Data Mining that deals with the problem of extract knowledge from data blindly. Basically, clustering identifies similar data in a dataset and groups them in sets named clusters. The high number of clustering practical applications has made it a fertile research topic with several approaches. One recent method that is gaining popularity in the research community is Spectral Clustering (SC). It is a clustering method that builds a similarity graph and applies spectral analysis to preserve the data continuity in the cluster. This work presents a new algorithm inspired by SC algorithm, the Co-Evolutionary Multi-Objective Genetic Graph-based Clustering (CEMOG) algorithm, which is based on the Multi-Objective Genetic Graph-based Clustering (MOGGC) algorithm and extends it by introducing an adaptative number of clusters. CEMOG takes an island-model approach where each island keeps a population of candidate solutions for ki clusters. Individuals in the islands can migrate to encourage genetic diversity and the propagation of individuals around promising search regions. This new approach shows its competitive performance, compared to several classical clustering algorithms (EM, SC and K-means), through a set of experiments involving synthetic and real datasets.