A lot of practical data mining applications deal with settings where the goal is to help human experts find rare cases that are of interest to them. Fraud Detection, Intrusion Detection, Surveillance for security applications, Information Filtering, Recommender Systems are some examples of these applications. A common aspect among all of these problems is that they involve users (or experts) in an interactive classification setting, i.e. the experts are interacting with the results of the data mining system and in turn providing feedback that is valuable for the system. The competing goals of the data mining system are to make these experts more efficient and effective in performing their task as well as getting feedback that would allow it to improve itself over time. In this talk, I will describe this interactive data mining setting, give examples of case studies where this setting applies, and how data mining techniques help manage this tradeoff to build practical interactive systems that are not only useful but also improve