APA 6th Edition Markić, B. (2012). KNOWLEDGE DISCOVERY PROCESS FOR BUILDING CUSTOMER PROFILES. Informatologia, 45 (3), 184-193. Preuzeto s https://hrcak.srce.hr/87367
MLA 8th Edition Markić, Brano. "KNOWLEDGE DISCOVERY PROCESS FOR BUILDING CUSTOMER PROFILES." Informatologia, vol. 45, br. 3, 2012, str. 184-193. https://hrcak.srce.hr/87367. Citirano 16.10.2019.
Chicago 17th Edition Markić, Brano. "KNOWLEDGE DISCOVERY PROCESS FOR BUILDING CUSTOMER PROFILES." Informatologia 45, br. 3 (2012): 184-193. https://hrcak.srce.hr/87367
Harvard Markić, B. (2012). 'KNOWLEDGE DISCOVERY PROCESS FOR BUILDING CUSTOMER PROFILES', Informatologia, 45(3), str. 184-193. Preuzeto s: https://hrcak.srce.hr/87367 (Datum pristupa: 16.10.2019.)
Vancouver Markić B. KNOWLEDGE DISCOVERY PROCESS FOR BUILDING CUSTOMER PROFILES. Informatologia [Internet]. 2012 [pristupljeno 16.10.2019.];45(3):184-193. Dostupno na: https://hrcak.srce.hr/87367
IEEE B. Markić, "KNOWLEDGE DISCOVERY PROCESS FOR BUILDING CUSTOMER PROFILES", Informatologia, vol.45, br. 3, str. 184-193, 2012. [Online]. Dostupno na: https://hrcak.srce.hr/87367. [Citirano: 16.10.2019.]
Sažetak The knowledge about customer preferences and behavior is fundamental for personalization of products and service. Personalization products and services are possible only if we have enough knowledge of who customers are, how they are similar among, how they behave. Knowledge discovery is process of transforming data into knowledge by adequate algorithms and software tools. In the paper is developed an approach that uses data in the form of transactional databases to construct accurate individual profiles. In developed data model are integrated transactional data and rules describing customer’s behavior. The rules are extracted from transactional data and cover individual customer behavior as well as the common behavior of all customers in the market segment. There are two rules types: first, for describing individual customer behavior and second, for the market behavior. Knowledge discovery plays a crucial role as an enabler to the organizations to integrate effective analytical data mining methods for prediction, classification, cluster, anomaly detection with data management and information visualization. Knowledge discovery is oriented to learning. In the process of learning we are implementing the functions of R language and this tool has shown satisfactory application and development power.