 Datamining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Datamining is an interdisciplinary subfield of computer science with an overall goal to extract information with intelligent method from a data set and transform the information into a comprehensible structure for further use. Datamining is the analysis step of the knowledge discovery in databases process, or KDV. Aside from the raw analysis step, it also involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term datamining is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing collection, extraction, warehousing, analysis, and statistics as well as any application of computer decision support system, including artificial intelligence e.g., machine learning and business intelligence. The book Datamining, Practical Machine Learning Tools and Techniques with Java which covers mostly machine learning material was originally to be named just practical machine learning, and the term datamining was only added for marketing reasons. In the more general terms large-scale data analysis and analytics, or, when referring to actual methods, artificial intelligence and machine learning, are more appropriate. The actual datamining task is this ML automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records clustered analysis unusual records anomaly detection and dependencies association rule mining, sequential pattern mining. This usually involves using database techniques such as spatial indices. These patterns can and be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the datamining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the datamining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data phishing, and data snooping refer to the use of datamining methods to sample parts of a larger population data set that are or may be too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.