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Descriptive data mining (2nd ed.)
Olson D., Lauhoff G., Springer International Publishing, New York, NY, 2019. 130 pp.  Type: Book
Date Reviewed: Nov 2 2020

Data mining when applied in a business context aims to improve decision making and strategic advantages by providing a better understanding of customers, suppliers, employees, and other stakeholders. The algorithms behind data mining may be complex; however, with the right tools, data mining can be less intimidating to non-mathematical business professionals. This is the book’s aim: to provide simple explanations and demonstrations of some descriptive data mining concepts, methods, and tools, in a way someone with a business background can understand. And it succeeds to a great extent. The book is direct and easy to read, explaining how methods work without in-depth scholarly references.

The book starts with a discussion of initial data exploration through visualization, and continues with a presentation of two basic marketing methods: market basket analysis, and recency, frequency, and monetary value (RFM) analysis. It then describes widely used data mining algorithms, association rules, and cluster analysis, enabling data analysts to identify general groupings in the data. Finally, it discusses link analysis, an analysis method for linked data like social networks. All of the methods are demonstrated using business-related data and supported by appropriate software tools. All of the tools were selected for widespread availability and access.

In spite of its strengths and many features, there is still room for improvement. The organization of the book seems a bit strange. The last chapter reads more like an introduction rather than a conclusion. An index would help readers locate important information, and links to the full datasets used would help them better understand the examples; both are missing here.

Overall, this is a suitable book for data mining newcomers who are not interested in a theoretical understanding of the algorithms. Although exercises are not included, the book could be used as a course resource.

Reviewer:  Evangelia Kavakli Review #: CR147095 (2104-0073)
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Data Mining (H.2.8 ... )
 
 
Decision Support (H.4.2 ... )
 
 
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