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Encyclopedia of machine learning and data mining (2nd ed.)
Sammut C., Webb G., Springer International Publishing, New York, NY, 2017. 1335 pp. Type: Book (978-1-489976-85-7)
Date Reviewed: Jan 29 2018

The quest for developing intelligent machines has a long history. In the year 1950, Alan Turing ushered in his imitation game as a way of finding out if a machine could be regarded as being intelligent. Over the years, machine learning has integrated ideas from disciplines as varied as genetics, information theory, neuroscience, operations research, psychology, and statistics. Thus, it is hard for a novice to find his or her way through the machine learning jungle. With this in mind, Claude Sammut and Geoffrey Webb produced the Encyclopedia of machine learning in the year 2011.

The objectives of that reference work were to aid inquiries into the field of machine learning as a whole and to dig into specific topics, simultaneously supplying helicopter views and references to original literature. Needless to say, most of the entries were composed by people with specialist knowledge in their field. Refereeing was done by top machine learning researchers. Producing the encyclopedia, which had over 250 entries arranged in alphabetical order, was a major project. The reference work was published in print as well as online formats. The print publication included an index of topics and writers, whereas the online edition supplemented this with hyperlinks, CrossRef citations, and links to further research work. The encyclopedia had about 1050 pages. The editors enhanced the 2011 book adding concepts from data mining and upgraded the title as Encyclopedia of machine learning and data mining for the second edition published in the year 2017.

The revised second edition is the one reviewed here. It has 1335 pages and is priced at US $729 currently. The first edition has been very popular and has been cited over 385 times in the literature since its publication. The second edition can be expected to be even more popular. It has about 800 entries, many of which did not exist in the previous edition. The entries have also been updated. The second edition places emphasis on data mining, in addition to machine learning, unlike the first edition, which focused only on machine learning.

The topics covered in the revised edition include applications, data mining, evolutionary computation, graph mining, information theory, learning and logic, pattern mining, reinforcement learning, relational mining, statistical learning, and text mining. As before, the topics were picked by an international advisory board composed of eminent researchers, and the entries were peer-reviewed. Many of the entries include applications, definitions, examples, keywords, and references to the literature. The entries are written in an expositive manner and are similar to tutorials. It need not be emphasized that the fields of data mining and machine learning have myriad applications, especially in newly emerging disciplines such as data science. The first edition of the encyclopedia was a useful resource for practitioners and those in academia and research. The second revised edition with extra information on data mining will have an even wider audience. The revised edition covers recent developments such as deep learning.

Some readers might seek an explanation for the rationale in including data mining along with machine learning. The publisher (Springer) maintains a continuously updated online edition of the encyclopedia, which they call a living reference. I recommend the encyclopedia as a valuable resource for libraries; however, its high price could be a deterrent to its adoption.

Reviewer:  S. V. Nagaraj Review #: CR145815 (1805-0215)
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