An Efficient Modified K-Means and Artificial Bee Colony Algorithm for Mining Search Result from Web Database - Sabitha, V; S.K. Srivatsa, Dr (International Journal of Engineering and Technology, 2018)
Nowadays, data growth is directly proportional to time and it is a challenge to store the data as well as retrieve the data in an organized fashion. The main goals of a web data clustering algorithm are to produce appropriate clusters for the end user, to assign the available data to the most relevant cluster, to respond the end user instantly. In this paper, we propose a new algorithm namely ‘An Efficient              Modified K-Means and Artificial Bee Colony Algorithm’ to cluster web data. The proposed algorithm is the combination of K-means and Artificial Bee Colony (ABC) algorithm. The reasons for incorporating k-means algorithm are its simplicity and efficiency [9].             Initially, ABC algorithm is employed to achieve clustering [10] and this is followed by the application of k-means algorithm. The initial cluster centre is fixed by ABC algorithm. On experimental analysis, it is proved that the performance of An Efficient Modified K-Means and Artificial Bee Colony Algorithm is better than the other comparative algorithms, in terms of precision and recall. An annotation wrapper for the search site is automatically constructed and can be used to annotate new result pages from the same web database. From the annotated search result, frequently used websites are identified by using apriori Algorithm which involve pattern mining. The              advantage of this new technique is fast operation on dataset containing items and provides facilities to avoid unnecessary scans to the database.