A novel algorithm for enhancing search results by detecting dissimilar patterns based on correlation method

The dynamic collection and voluminous growth of information on the web poses great challenges for retrieving relevant information. Though most of the researchers focused their research work in the areas of information retrieval and web mining, still their focus is only on retrieving similar patterns leaving dissimilar patterns which are likely to contain the outlying data. So this paper concentrates on mining web content outliers which extract the dissimilar web documents taken from the group of documents of same domain. Mining web content outliers indirectly help in promoting business activities and improving the quality of the search results. Existing algorithms for web content outliers mining are developed for structured documents, whereas, World Wide Web (WWW) contains mostly unstructured and semi structured documents. Therefore, there is need to develop a technique to mine outliers for unstructured and semi structured document types. In this research work, a novel statistical approach based on correlation method is developed for retrieving relevant web document through outlier detection technique. In addition, this method also identifies the redundant web documents. Removal of both redundant and outlaid documents improves the quality of search results catering to the user needs. Evaluation of the correlation method using Normalized Discounted Cumulative Gain method (NDCG) gives search results above 90%. The experimental results proved that this methodology gives better results in terms of accuracy, recall and specificity than the existing methodologies.