Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data m

Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data mining problems. But there is no single source that identifies which techniques solve what problems and how, the advantages and limitations, and real-life use-cases. Lately, identifying data mining techniques and corresponding problems that they solve has drawn significant attention. In this paper, the author describes the progress made in developing data mining techniques and then classify them in terms of data mining problems taxonomy to help assist practitioners in using appropriate data mining techniques that solve business problems. This will allow researchers to expand the body of knowledge in this discipline. This article proposes a data mining problems taxonomy based on data mining techniques being used. Prominent data mining problems include classification, optimization, prediction, partitioning, relationship, pattern matching, recommendation, ranking, sequential patterns and anomaly detection. The data mining techniques that are used to solve these data mining problems in general fall under top 10 data mining algorithms.