Data mining is the process of discovering insightful, informative, and new patterns from big-scale data as well as descriptive, perfectly reasonable, and predictive models. We start this chapter by staring at the basic data properties as a data matrix.
We highlight the geometric and algebraic views, as well as the probabilistic analysis of data. We then discuss the key data mining tasks that span exploratory data analysis, repeated pattern mining, clustering, and classification, setting out the book’s road map.
Learn how to analyze data and apply it to real-world data sets. This updated latest edition is used as an introduction to methods and models of data mining, including rules of the association, clustering, neural networks, logistic regression, and multivariate analysis. The authors use a unified “white box” approach to methods and models of data mining.
This approach is intended to help readers understand how to use small data sets and learn about the different methods and their complexities while providing them with an insight into the internal work of the studied process.
The chapters give readers practical analysis problems, which give readers a chance to use their newly-acquired data mining expertise in order to solve real problems by means of large, real-world data sets.
Data Mining and Predictive Analytics:
*Offers extensive coverage of association regulations, clustering, neural networks, regression logistics, and multivariate analysis as well as R programming language statistics.
*This includes more than 750 exercises in chapters, enabling readers to understand the new material.
*It provides a detailed case study that describes learning from the text.
*The company website includes access for computer science and statistical students as well as students in MBA programs and chief executive officers, a www.dataamining adviser, and an exclusive password-protected trainer.
#Data Mining and predictive analysis.
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