An Intelligent Clustering Approach for Improving Search Result of a Website

These days, the internet has become part of our life, and thus web data usage has increased tremendously. We proposed a model that will improve the search result using clustering approach. Clustering is being used to group the data into the relevant folder so that accessing of information will be fast. The K-means clustering algorithm is very efficient in terms of speed and is suitable for large dataset. However, K-means algorithm has some drawbacks, such as the number of clusters need to be defined in starting itself, initialisation affects the output, and it often gets stuck to local optima. We proposed a hybrid model that determines the number of clusters itself and gives global optimal result. The number which has been obtained is passed as a parameter for the K-means. Thus, our novel hybrid model integrates the features of K-means and genetic algorithm. The model will have the best characteristics of K-means and genetic algorithm, and overcomes the drawbacks of K-means and genetic algorithm.