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Cites:

Information Journal Paper

Title

Presenting a Model to Predict Business Intelligence Domain

Pages

  55-76

Abstract

 In recent years, the volume of data and information exchanged over web pages, social networks, emails, and blogs is increasing. Most of the information that is exchanged and stored is in text format, which is very valuable given the huge amount of text data that is analyzed and discovered from these data. Text Mining is one of the most important methods to extract knowledge from Structured Data which help organizations to achieve their goals. These days most of products like books purchased via internet or online. So that customers need dedicated information to make the decision to buy products more intelligently and smartly. Amazon is one of trading website which give related information to their customer for online buying. One of problem for these method for buying books is subcategory of books is not cleared which customer may buy unwanted books. In this research we tried to present predicting model to predict topic related to Business Intelligence by using Text Mining methods. Results show that Business Intelligence tools and visualization, behavioral organization, process management, decision support system, leadership, Text Mining and database are seven clusters which related to Business Intelligence.

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  • Cite

    APA: Copy

    ABBASI, FATEMEH, SOHRABI, BABAK, KHADIVAR, AMENEH, & MANIAN, AMIR. (2017). Presenting a Model to Predict Business Intelligence Domain. IRANIAN JOURNAL OF INFORMATION MANAGEMENT, 3(1 (4) ), 55-76. SID. https://sid.ir/paper/262675/en

    Vancouver: Copy

    ABBASI FATEMEH, SOHRABI BABAK, KHADIVAR AMENEH, MANIAN AMIR. Presenting a Model to Predict Business Intelligence Domain. IRANIAN JOURNAL OF INFORMATION MANAGEMENT[Internet]. 2017;3(1 (4) ):55-76. Available from: https://sid.ir/paper/262675/en

    IEEE: Copy

    FATEMEH ABBASI, BABAK SOHRABI, AMENEH KHADIVAR, and AMIR MANIAN, “Presenting a Model to Predict Business Intelligence Domain,” IRANIAN JOURNAL OF INFORMATION MANAGEMENT, vol. 3, no. 1 (4) , pp. 55–76, 2017, [Online]. Available: https://sid.ir/paper/262675/en

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