'. . . Knowledge Discovery is the most desirable end-product of computing. Finding new phenomena or enhancing our knowledge about them has a greater long-range value than optimizing production processes or inventories, and is second only to task that preserve our world and our environment. It is not surprising that it is also one of the most difficult computing challenges to do well . . .' (Wiederhold, 1996). The main objective of knowledge discovery in Data Mining lies in the finding of data patterns. The knowledge about the current customers can be used to predict profitable customers based on their personal information. This explorative report focuses on analysing different methods of data mining to predict profitable customers of a dating site. The second key aspect is to match individual customers based on their personal information. The dataset analysed is derived from the customer database of Australia's largest dating site with over 1.9 million members. The dataset contains static activity and dynamic activity. Static activity includes all personal, demographic and interest information entered by the customer at its registration. The emails sent, channels communicated and kisses sent describe the dynamic activity.

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Project Report from the year 2010 in the subject Computer Science - Commercial Information Technology, grade: 1,0, Queensland University of Technology, course: Data Mining, language: English, abstract: '. . . Knowledge Discovery is the most desirable end-product of computing. Finding new phenomena or enhancing our knowledge about them has a greater long-range value than optimizing production processes or inventories, and is second only to task that preserve our world and our environment. It is not surprising that it is also one of the most difficult computing challenges to do well . . .' (Wiederhold, 1996). The main objective of knowledge discovery in Data Mining lies in the finding of data patterns. The knowledge about the current customers can be used to predict profitable customers based on their personal information. This explorative report focuses on analysing different methods of data mining to predict profitable customers of a dating site. The second key aspect is to match individual customers based on their personal information. The dataset analysed is derived from the customer database of Australia's largest dating site with over 1.9 million members. The dataset contains static activity and dynamic activity. Static activity includes all personal, demographic and interest information entered by the customer at its registration. The emails sent, channels communicated and kisses sent describe the dynamic activity.