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Effective Probabilistic Model for Webpage Classification

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Emerging Trends in Computing and Communication

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 298))

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Abstract

World Wide Web (www) is a large repository of information which contains a plethora of information in the form of web documents. Information stored in web is increasing at a very rapid rate and people rely more and more on Internet for acquiring information. Internet World Stats reveal that world Internet usage has increased by 480 % within the period 2000–2011. This exponential growth of the web has made it a difficult task to organize data and to find it. If we categorize data on the Internet, it would be easier to find relevant piece of information quickly and conveniently. There are some popular web directories projects like yahoo directory and Mozilla directory in which web pages are organized according to their categories. According to a recent survey, it has been estimated that about 584 million websites are currently hosted on the Internet. But these Internet directories have only a tiny fraction of websites listed with them. The proper classification has made these directories popular among web users. However these web directories make use of human effort for classifying web pages and also only 2.5 % of available webpages are included in these directories. Rapid growth of web has made it increasingly difficult to classify web pages manually, mainly due to the fact that manually or semi-automatic classification of website is a tedious and costly affair. Because of this reason web page classification using machine learning algorithms has become a major research topic in these days. A number of algorithms have been proposed for the classification of web sites by analyzing its features. In this paper we will introduce a fast, effective, probabilistic classification model with a good accuracy based on machine learning and data mining techniques for the automated classification of web-pages into different categories based on their textual content.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, 110025, India

    Hammad Haleem, C. Niyas, Siddharth Verma, Akshay Kumar & Faiyaz Ahmad

Authors
  1. Hammad Haleem
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  2. C. Niyas
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  3. Siddharth Verma
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  4. Akshay Kumar
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  5. Faiyaz Ahmad
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Corresponding author

Correspondence to Hammad Haleem .

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Editors and Affiliations

  1. Department of Information Technology, BP Poddar Institute of Management and Technology, Kolkata, West Bengal, India

    Sabnam Sengupta

  2. Department of Information Technology, BP Poddar Institute of Management and Technology, Kolkata, West Bengal, India

    Kunal Das

  3. Department of Information Technology, BP Poddar Institute of Management and Technology, Kolkata, West Bengal, India

    Gitosree Khan

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Haleem, H., Niyas, C., Verma, S., Kumar, A., Ahmad, F. (2014). Effective Probabilistic Model for Webpage Classification. In: Sengupta, S., Das, K., Khan, G. (eds) Emerging Trends in Computing and Communication. Lecture Notes in Electrical Engineering, vol 298. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1817-3_29

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  • DOI: https://doi.org/10.1007/978-81-322-1817-3_29

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1816-6

  • Online ISBN: 978-81-322-1817-3

  • eBook Packages: EngineeringEngineering (R0)

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