Identification of interested web users using decision tree classifier - Mallik, Moksud Alam; Zulkurnain, Nurul Fariza; Jamil Ahmed, S. K.; Nizamuddin, Mohammed Khaja (Springer Nature Singapore, 2021)
The development of the World Wide Web has evolved in an enormous volume of data; consequently, drawing out of useful knowledge is a demanding research issue. Use of data mining methods to the Web mentioned as Web mining. It goals at finding and extracting hidden knowledge from Web pages, and services bring out useful information from Web resources and discover common patterns on the Web for examining user activities in network-based systems. The key aim of this paper is to find interested users for a particular website. Hence, the requirement is to build a classification model that can categorize users into interested and non-interested visitors for a particular website based on their access pattern using Web server logs by making use of decision tree classification technique. It will be useful for website owners, to get potential users who are more interested in their website, so that the administration can provide higher privileges for them. Users will be benefited by the special service, which is not available for other users.