Water Wave Optimization Based Data Clustering Model

This paper presents data clustering model by adopting water wave optimization (WWO) algorithm. In recent times, metaheuristics have gained significance to improve the efficiency of clustering algorithms. Cluster accuracy results express the effectiveness of the clustering algorithm. In this work, WWO is adopted to improve the accuracy for data clustering. On the basis of WWO, clustering model has been proposed. The proposed algorithm aims to improve data clustering accuracy. Several standard datasets from UCI repository are considered for assessing the simulation results and results are evaluated using accuracy and f-score. The Friedman test is applied for statistical analysis to validate the proposed model. Experimental results proved that proposed clustering model succeeds to achieve higher accuracy rate.