@article{MAKHILLIJSC201914221463,
    title = {Text Document Clustering using Hashing Deep Learning Method},
    journal = {International Journal of Soft Computing},
    volume = {14},
    number = {2},
    pages = {44-52},
    year = {2019},
    issn = {1816-9503},
    doi = {ijscomp.2019.44.52},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2019.44.52},
    author = {Nahrain,Shawkat and},
    keywords = {Web news mining,deep learning,LSTM,hash,geolocation},
    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.}
    }