R. K. Roul

R. K. Roul, O. R. Devanand, S. K. Sahay

The dynamic web has increased exponentially over the past few years with more than thousands of documents related to a subject available to the user now. Most of the web documents are unstructured and not in an organized manner and hence user facing more difficult to find relevant documents. A more useful and efficient mechanism is combining clustering with ranking, where clustering can group the similar documents in one place and ranking can be applied to each cluster for viewing the top documents at the beginning.. Besides the particular clustering algorithm, the different term weighting functions applied to the selected features to represent web document is a main aspect in clustering task. Keeping this approach in mind, here we proposed a new mechanism called Tf-Idf based Apriori for clustering the web documents. We then rank the documents in each cluster using Tf-Idf and similarity factor of documents based on the user query. This approach will helps the user to get all his relevant documents in one place and can restrict his search to some top documents of his choice. For experimental purpose, we have taken the Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and use gensim toolkit to carry out our work. We have compared our approach with traditional apriori algorithm and found that our approach is giving better results for higher minimum support. Our ranking mechanism is also giving a good F-measure of 78%.

R. K. Roul, S. K. Sahay

Exponential growth of the web increased the importance of web document classification and data mining. To get the exact information, in the form of knowing what classes a web document belongs to, is expensive. Automatic classification of web document is of great use to search engines which provides this information at a low cost. In this paper, we propose an approach for classifying the web document using the frequent item word sets generated by the Frequent Pattern (FP) Growth which is an association analysis technique of data mining. These set of associated words act as feature set. The final classification obtained after Naïve Bayes classifier used on the feature set. For the experimental work, we use Gensim package, as it is simple and robust. Results show that our approach can be effectively classifying the web document.

R. K. Roul, S. K. Sahay

Search engine returns thousands of web pages for a single user query, in which most of them are not relevant. In this context, effective information retrieval from the expanding web is a challenging task, in particular, if the query is ambiguous. The major question arises here is that how to get the relevant pages for an ambiguous query. We propose an approach for the effective result of an ambiguous query by forming community vector based on association concept of data minning using vector space model and the freedictionary. We develop clusters by computing the similarity between community vectors and document vectors formed from the extracted web pages by the search engine. We use Gensim package to implement the algorithm because of its simplicity and robust nature. Analysis shows that our approach is an effective way to form clusters for an ambiguous query.