Web spam is a technique through which the irrelevant pages get higher rank than relevant pages in the search engine's results. Spam pages are generally insufficient and inappropriate results for user. Many researchers are working in this area to detect the spam pages. However, there is no universal efficient technique developed so far which can detect all spam pages. This paper is an effort in that direction, where we propose a combined approach of content and link-based techniques to identify the spam pages. The content-based approach uses term density and Part of Speech (POS) ratio test and in the link-based approach, we explore the collaborative detection using personalized page ranking to classify the Web page as spam or non-spam. For experimental purpose, WEBSPAM-UK2006 dataset has been used. The results have been compared with some of the existing approaches. A good and promising F-measure of 75.2% demonstrates the applicability and efficiency of our approach.

No abstract

No abstract

The Traditional apriori algorithm can be used for clustering the web documents based on the association technique of data mining. But this algorithm has several limitations due to repeated database scans and its weak association rule analysis. In modern world of large databases, efficiency of traditional apriori algorithm would reduce manifolds. In this paper, we proposed a new modified apriori approach by cutting down the repeated database scans and improving association analysis of traditional apriori algorithm to cluster the web documents. Further we improve those clusters by applying Fuzzy C-Means (FCM), K-Means and Vector Space Model (VSM) techniques separately. We use Classic3 and Classic4 datasets of Cornell University having more than 10,000 documents and run both traditional apriori and our modified apriori approach on it. Experimental results show that our approach outperforms the traditional apriori algorithm in terms of database scan and improvement on association of analysis.

The dynamic Web, which contains huge number of digital documents, is expanding day by day. Thus, it has become a tough challenge to search for a particular document from such a large volume of collections. Text classification is a technique which can speed up the search and retrieval tasks and hence is the need of the hour. Aiming in this direction, this study proposes an efficient technique that uses the concept of connected component (CC) of a graph and Wordnet along with four established feature selection techniques [e.g., TF-IDF, Chi-square, Bi-Normal Separation (BNS) and Information Gain (IG)] to select the best features from a given input dataset in order to prepare an efficient training feature vector. Next, multilayer extreme learning machine (ML-ELM) (which is based on the architecture of deep learning) and other state-of-the-art classifiers are trained on this efficient training feature vector for classification of text data. The experimental work has been carried out on DMOZ and 20-Newsgroups datasets. We have studied the behavior and compared the results of different classifiers using these four important feature selection techniques used for classification process and observed that ML-ELM achieved the maximum overall F-measure of 72.28 % on DMOZ dataset using TF-IDF as the feature selection technique and 81.53 % on 20-Newsgroups dataset using BNS as the feature selection technique compared to other state-of-the-art classifiers which signifies the usefulness of deep learning used by ML-ELM for classifying the text data. Experimental results on these Communicated by A. Di Nola.