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  • Course Information
    • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Data Mining BBY412 FALL-SPRING 2+0 E 5
    Learning Outcomes
    1-Learns data processing, classification, clustering, and analysis with data mining techniques.
    2-To be informed about data warehouses
    3-Compares data and text mining.
    Prerequisites -
    Language of Instruction Turkish
    Responsible Assoc. Dr. Kasım BİNİCİ
    Instructors -
    Assistants -
    Resources K1-Akpınar, H. (2014). Data: Veri Madenciliği Veri Analizi. Papatya. Akpınar, H. (2014). Data: Veri Madenciliği Veri Analizi. Papatya.
    Supplementary Book SR1. Binici, K. (2018). Kütüphane ve Bilgi Biliminde Tema ve Yönelim. İstanbul: Hiperyayın.
    SR2. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. ve Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide. SPSS. https://the-modeling-agency.com/crisp-dm.pdf adresinden erişildi.
    SR3. Gürsakal, N. (2014). Büyük Veri. Bursa: Dora.
    SR4. Miner, G., Delen, D., Elder, J., Fast, A., Hill, T. ve Nisbet, R. A. (2012). Practical text mining and statistical analysis for non-structured text data applications. Waltham, MA: Academic Press.
    SR5. North, M. (2012). Data mining for the masses. http://rapidminer.com/wp-content/uploads/2013/10/DataMiningForTheMasses.pdf adresinden erişildi.
    SR6. Pektaş, A. O. (2013). SPSS ile Veri Madenciliği. Dikeyeksen.
    SR7. Şeker, Ş. E. (2013). İş Zekası ve Veri Madenciliği. Cinius.
    Goals To introduce the data mining techniques and give information about applications that processed on data structures
    Content In this course, data mining process, appropriate software in data mining, data mining models, sample research models and applications will be discussed.
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