Authors : Nahrain A. Swidan,, Shawkat K. Guirguis and Omar G. Abood
Abstract: Web mining is the method of analyzing an grouping of behavioral, statistic, way of life, value-based, web and geographic data for the personalization of offers to online shoppers in genuine time. The goal of this study is to build an effective model for the use of hybrid data clustering and classification technology to evaluate online news data. Assess the best way to use site news information algorithms and assess the reliability of the online news databases use tools and techniques for data mining. A well-known platform to share information among online users is a web-based application. However, nowadays, it is the most challenge to handle gigantic data or enormous information such as web news or web-based promoting by users. On the other side, web applications are the most readily available medium for consumers to access up-to-date information. Such apps also need tremendous space, time and drain the battery power of the mobile devices of the users. One solution to mitigate these challenges is therefore, to extract or extract certain information on the basis of certain characteristics. In contrast, the attributes are the actions or the information collected from different sources by the consumer. This essay attempts to design and implement a web app to extract information on geolocation and space and provides a comparative study on three specific mining techniques.
Nahrain A. Swidan,, Shawkat K. Guirguis and Omar G. Abood, 2019. Text Document Clustering using Hashing Deep Learning Method. International Journal of Soft Computing, 14: 44-52.