A taxonomy of data mining problems

The development of effective data mining algorithms has dominated most of the research in data mining and knowledge discovery. Researchers and practitioners to address a variety of real-world data mining issues have created data mining methods. However, there is no one source that specifies which methods address which issues and how, as well as their benefits and drawbacks, and real-world applications. Identifying data mining methods and the issues that they address has recently gotten a lot of attention. The author of this article discusses the progress achieved in creating data mining methods and then categorizes them using a data mining issues taxonomy to aid practitioners in utilizing suitable data mining approaches to address business problems. Researchers will be able to add to the corpus of knowledge in this field because of this. Based on the data mining methods employed, this article offers a taxonomy for data mining issues. Classification, optimization, prediction, partitioning, relationship, pattern matching, recommendation, ranking, sequential patterns, and anomaly detection are all common data mining issues. In general, the data mining methods utilized to address these data mining issues are classified as top 10 data mining algorithms.