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Data mining is the process of extracting and detecting patterns in huge data sets by utilizing approaches that lie at the confluence of machine learning, statistical analysis, and database management systems. Data mining is an interdisciplinary subject of computer science and statistics with the overarching goal of extracting information from a data set and translating the information into a structure that is understandable for the sake of subsequent application. The "knowledge discovery in databases" (also known as "KDD") method includes an analysis step that is known as "data mining." In addition to the phase of raw analysis, it also includes aspects of database management and data management, data pre-processing, model and inference considerations, interestingness measures, complexity considerations, post-processing of newly discovered structures, visualization, and online updating.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Data mining
Chapter 2: Machine learning
Chapter 3: Text mining
Chapter 4: Association rule learning
Chapter 5: Concept drift
Chapter 6: Weka (software)
Chapter 7: Special Interest Group on Knowledge Discovery and Data Mining
Chapter 8: Educational data mining
Chapter 9: Social media mining
Chapter 10: Outline of machine learning
(II) Answering the public top questions about data mining.
(III) Real world examples for the usage of data mining in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of data mining' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of data mining.
