Content based web spam detection using naive bayes with different feature representation technique

Web Spam Detection is the processing to organize the search result according to specified criteria. Most often this refers to the automatic processing of search result, but the term also applies to the automatic classification of search results into ham and spam. Our work also evaluates change in performance by using different representation for the document vector like term frequency (TF), Binary, inverse document frequency (IDF) and TF-IDF. There are various Benchmark Datasets available for researchers related to web spam filtering. There has been significant effort to generate public benchmark datasets for antiweb spam filtering. One of the main concerns is how to protect the privacy of the users whose ham links are included in the datasets. We perform a statistical analysis of a large collection of WebPages, focusing on spam detection. Dimension reduction is important part of classification because it provides ease to visualize high dimensional data. This work reduce dimension of training data in 2D and full and mapped training and test data in to vector space. There are several classification here we use Naive Bayes classification and train data set with varying different representation and testing perform with different spam ham ratio Key-Words: Content spam, keyword count, variety, density and Hidden or invisible text