Published Paper Details:

Today every process requires very important as well as updated data about that process or work. In order to acquire those data, employees from various fields searching through different search engines. Only very few times search engines are helping to get the users expected data but many times it can provides only the approximate data about the user expectation. To avoid this state we used the algorithm named K-means++ over the search engine documents to extract the most relevant information, because this algorithm uses the special mathematical method to find the out the successive cluster center of each cluster documents and only the first center is random selection from the data unlike K-means algorithm, which randomly selects all the cluster centers. Segmentation fusion is applied to provide the most resultant list, which is accepting as input of each cluster�s documents gradually. Performance of the K-means++ algorithm is compared and evaluated with the measures like purity and F-measure, which shows the novel algorithm has providing better relevant result while comparing with other traditional algorithms. Finally, this algorithm always works with large data set, so the researchers can utilize this algorithm to their innovative ideas comes to reality with high precision and recall.