![]() ![]() GEOGRAPHIC DATA MINING AND KNOWLEDGE DISCOVERY,Ĭristóbal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan S.J.d. WagstaffĬONTRAST DATA MINING: CONCEPTS, ALGORITHMS, AND APPLICATIONSĭATA CLASSIFICATION: ALGORITHMS AND APPLICATIONSĭATA CLUSTERING: ALGORITHMS AND APPLICATIONSĭATA CLUSTERING IN C++: AN OBJECT-ORIENTED APPROACHĭATA MINING: A TUTORIAL-BASED PRIMER, SECOND EDITIONĭATA MINING WITH R: LEARNING WITH CASE STUDIES, SECOND EDITIONĮVENT MINING: ALGORITHMS AND APPLICATIONS Chawla, and Simeon SimoffĬOMPUTATIONAL METHODS OF FEATURE SELECTIONĬONSTRAINED CLUSTERING: ADVANCES IN ALGORITHMS, THEORY, SrivastavaĬOMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR SUSTAINABLE Visualization, data mining systems and tools, and privacy and security issues.ĪCCELERATING DISCOVERY: MINING UNSTRUCTURED INFORMATION FORĪDVANCES IN MACHINE LEARNING AND DATA MINING FOR ASTRONOMY Methods and applications, modeling, algorithms, theory and foundations, data and knowledge RAPIDMINER STUDIO 6.5 DOWNLOAD SERIESSeries includes, but is not limited to, titles in the areas of data mining and knowledge discovery ![]() The inclusion of concrete examples and applications is highly encouraged. Techniques through the publication of a broad range of textbooks, reference works, and handbooks. Series encourages the integration of mathematical, statistical, and computational methods and This series aims to capture new developments and applications in data mining and knowledgeĭiscovery, while summarizing the computational tools and techniques useful in data analysis. ![]() This allows the reader maximum flexibility for their hands-on data mining experience.ĭata Mining and Knowledge Discovery Seriesĭepartment of Computer Science and Engineering RAPIDMINER STUDIO 6.5 DOWNLOAD SOFTWAREBoth software tools are used for stepping students through the tutorials depicting the knowledge discovery process. The text provides in-depth coverage of RapidMiner Studio and Weka's Explorer interface. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |