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Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional d. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated metho. An essential text for readers wishing to use data mining methods to cope with management and engineering design problems. This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound ma.
Exquisite quality pursuer in crushing and milling industry, and drafting unit of mill industry standards. For more than 30 years, it has focused on the research and development and manufacturing of mining crushing equipment, construction crushing equipment, industrial milling equipment and green building materials equipment, and providing professional solutions and mature supporting products to create value for customers. Kantardzic has won awards for several of his papers, has been published in numerous referred. Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Mehmed Kantardzic.
December 1, December ; 5 4 : — This book provides an interesting, readable, and comprehensive treatment of the field of data mining for a reader who is not familiar with the concepts, tools, and algorithms. The book provides a nice introduction to the field and discusses standard algorithms and data processing techniques. The emphasis of the book is on data sources that can be transformed into tabular data. Thus, the issues regarding mining unstructured data sets or direct mining of relational databases are not discussed in detail. With few exceptions, the book does not cover many of the more recent developments in this and related fields.
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Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: 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 data mining was only added for marketing reasons. The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records cluster analysis , unusual records anomaly detection , and dependencies association rule mining , sequential pattern mining. This usually involves using database techniques such as spatial indices.
Modern science and engineering are based on using first — principle models to describe physical, biological, and social systems. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Data Mining uses raw data to extract information or in fact, mining the required information from data.
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