Analýza firemních dat o vytíženosti služeb s využitím dolování dat

This work falls within the field of acquiring knowledge from databases. Data mining takes place on real business data about the use of their services mainly related to their products. Three tasks were selected that are potentially useful to the business and should help in making decisions. The first task involves obtaining association rules from products. The second task experiments with different classification methods and verifies which ones give the best results to predict various customer purchases of concrete product with certain parameters. The last task falls into the clustering area where remote values (customers) are searched within the data of their requests. Association rules mining using algorithms FP Growth and Apriori is implemented in the resulting application. Also, the second task is implemented in the application and the Naive Bayes prediction model is used.

Datamining, RapidMiner, mining knowledge discovery, analysis of Company Data, association rules, classification and prediction, clustering, Apriori, FP Growth, Naive Bayes