A Hybrid Approach for Web Document Clustering Using K-means and Artificial Bee Colony Algorithm
M. M. Gowthul Alam, S. Baulkani · International journal of intelligent engineering and systems · 2016
Nowadays data growth is directly proportional to time and it is a major challenge to store the data in an organised fashion.Document clustering is the solution for organising relevant documents together.In this paper, a web clustering algorithm namely WDC-KABC is proposed to cluster the web documents effectively.The proposed algorithm uses the features of both K-means and Artificial Bee Colony (ABC) clustering algorithm.In this paper, ABC algorithm is employed as the global search optimizer and K-means is used for refining the solutions.Thus, the quality of the cluster is improved.The performance of WDC-KABC is analysed with four different datasets (webkb, wap, rec0 and 7sectors).The proposed algorithm is compared with existing algorithms such as K-means, Particle Swarm Optimization, Hybrid of Particle Swarm Optimization and K-means and Ant Colony Optimization.The experimental results of WDC-KABC are satisfactory, in terms of precision, recall, f-measure, accuracy and error rate.