Data mining and statistics for decision making download

Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book. Apple Books Preview. Publisher Description. MLA Dasgupta, Hirak. Dasgupta, H. Trivedi, S. Dey, A. Panda Eds. IGI Global. Dasgupta, Hirak.

Available In. DOI: Current Special Offers. No Current Special Offers. Abstract In the age of information, the world abounds with data. In order to obtain an intelligent appreciation of current developments, we need to absorb and interpret substantial amounts of data. The amount of data collected has grown at a phenomenal rate over the past few years. The computer age has given us both the power to rapidly process, summarize and analyse data and the encouragement to produce and store more data.

The aim of data mining is to make sense of large amounts of mostly unsupervised data, in some domain. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical This text features a wealth of real data applications, with coverage of current issues including ethics and data mining.

Data mining techniques: for marketing, sales, and customer relationship management. Data mining and statistics for decision making. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules.

They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques.

Starts from basic principles up to advanced concepts. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring.

Supported by an accompanying website hosting datasets and user analysis.