Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner

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Product description
Includes data-rich case studies and end-of-chapter exercises to build practical and theoretical understanding of key data mining methods and techniques. New chapters on social network analysis and text mining
Includes detailed summaries outlining key topics, data-rich case studies to illustrate data mining applications, and end-of-chapter exercises
Offers over two dozen case studies and includes innovative material on text analytics, recommender systems, social network analysis, getting data from a database into the analytics process, and scoring and deploying the results of an analysis to a database. Includes separate chapters that each treat k-nearest neighbors and Naïve Bayes methods.

Audience level
Intermediate/Advanced
Intermediate/Advanced
Intermediate/Advanced

Suitable for use in higher education courses?
✓
✓
✓

Professional application
Reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology
Reference for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field
Reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology who have a special interest in R.

Related software
XLMiner (Add-in to Microsoft Office Excel and part of Analytic Solver)
JMP Pro (Statistical package from the SAS Institute)
R (Freely available for download)

Trial license included with text
✓

Authors
Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Galit Shmueli, Peter C. Bruce, Mia L. Stephens, Nitin R. Patel
Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Casey Lichtendahl

Content length
552 pages
480 pages
448 pages

Publisher ‏ : ‎ John Wiley & Sons Inc; 3rd edition (January 1, 2016)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 514 pages
ISBN-10 ‏ : ‎ 1118729277
ISBN-13 ‏ : ‎ 978-1118729274
Item Weight ‏ : ‎ 2.44 pounds
Dimensions ‏ : ‎ 7.5 x 1.25 x 10.25 inches

Data mining is a powerful tool for businesses to extract valuable insights from large datasets. XLMiner is a popular data mining software that offers a wide range of tools and functionalities for business analytics. In this post, we will explore the concepts, techniques, and applications of data mining in XLMiner.

Concepts:
Data mining involves the process of discovering patterns, trends, and relationships in large datasets. XLMiner uses various algorithms and methods to analyze data and extract meaningful insights. Some key concepts in data mining include clustering, classification, regression, association rules, and anomaly detection.

Techniques:
XLMiner offers a variety of data mining techniques to help businesses make informed decisions. These techniques include decision trees, neural networks, support vector machines, k-means clustering, and association rules mining. Each technique has its own strengths and weaknesses, and businesses can choose the most suitable technique based on their specific needs.

Applications:
Data mining has a wide range of applications in business analytics. Some common applications of data mining in XLMiner include customer segmentation, market basket analysis, fraud detection, churn prediction, and demand forecasting. By leveraging the power of data mining, businesses can gain a competitive advantage, improve decision-making, and drive business growth.

Overall, data mining is an essential tool for businesses looking to unlock the potential of their data. XLMiner provides a user-friendly interface and a wide range of tools to help businesses analyze data and extract valuable insights. By mastering the concepts, techniques, and applications of data mining in XLMiner, businesses can drive success and achieve their goals in today’s data-driven world.
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