Novel frequent sequential patterns based probabilistic model for effective classification of web documents

Web page classification has been one of essential tasks in web information retrieval such as delivering content specific search results, focused crawling and maintaining web-directory projects like DMOZ, etc. This paper presents a novel probabilistic web page classification scheme that utilizes the occurrences of frequent sequential patterns to determine the class of the document. As being suggested by many previous works in the field of text mining, patterns possess more relevant information about the document than individual words. This paper is an attempt to successfully make use of this hypothesis for classification of web documents. After testing this novel approach on RCV1 dataset, we were able to obtain classify the test documents with 88% accuracy.