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    <name>Article</name>
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          <name>Title</name>
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              <text>Mining the web data for classifying and predicting users' requests</text>
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          <description>The topic of the resource</description>
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              <text>Classification; Customer; E-commerce; Prediction; Product Recommendation; Segmentation; Trend</text>
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              <text>Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client?s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands. Copyright  2018 Institute of Advanced Engineering and Science. All rights reserved.</text>
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          <name>Creator</name>
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              <text>Girish S.; Ramamurthy B.; Senthilnathan T.</text>
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          <name>Source</name>
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              <text>International Journal of Electrical and Computer Engineering, Vol-8, No. 4, pp. 2390-2398.</text>
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          <name>Publisher</name>
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              <text>Institute of Advanced Engineering and Science</text>
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              <text>2018-01-01</text>
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              <text>&lt;a href="https://doi.org/10.11591/ijece.v8i4.pp2390-2398" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.11591/ijece.v8i4.pp2390-2398&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049571427&amp;amp;doi=10.11591%2Fijece.v8i4.pp2390-2398&amp;amp;partnerID=40&amp;amp;md5=1a91b08884f0e6b1ef3f2be356ef3aab" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049571427&amp;amp;doi=10.11591%2fijece.v8i4.pp2390-2398&amp;amp;partnerID=40&amp;amp;md5=1a91b08884f0e6b1ef3f2be356ef3aab&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>All Open Access; Green Open Access; Hybrid Gold Open Access</text>
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              <text>ISSN: 20888708</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Girish S., Department of Computer Science, Christ University, Hosur Road, Bengaluru, Karnataka, India; Ramamurthy B., Department of Computer Science, Christ University, Hosur Road, Bengaluru, Karnataka, India; Senthilnathan T., Department of Computer Science, Christ University, Hosur Road, Bengaluru, Karnataka, India</text>
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