Improving search result clustering using nature inspired approach

The massive internet and web data are increasing day-by-day rapidly, so searching an important information from huge data is a tedious task. Relevant document retrieval for polysemy queries is a primary issue. This paper addressed the polysemy issue for information retrieval and proposed an intelligent FK Hybrid Model. The model used clustering as a tool to group search results into a coherent group that may help the user in effective and efficient search result during browsing. We utilize the traditional clustering algorithm’s capabilities and integrate a nature-inspired approach. The FK Hybrid Model combines the Firefly algorithm and the K-means clustering algorithm. In a series of experiments performed on AMBIENT data, the suggested model significantly outperforms to the related methods.. The experimental results show that our model can locate appropriate clustering solutions with the correct number of clusters.