Browse Books

Reviewer: Edgar Weippl

This book is a comprehensive textbook on basic principles in data mining. Unlike many business-oriented books, the first part focuses on the mathematical foundations of data analysis. Classical approaches to exploring data, including principal component analysis and multi- dimensional scaling, are clearly and thoroughly explained (chapter 3). Relevant basics in statistics (maximum likelihood, Bayesian hypothesis testing) are covered in chapter 4. Chapter 5 provides an overview of fundamental data mining algorithms (CART, back propagation, a priori). The authors emphasize a clear distinction between models and patterns (chapter 6) and show how these structures can be fitted to the data (chapter 7). As the aforementioned topics indicate, the first part is focused on mathematics, whereas the second part mainly covers topics related to computer science. Chapter 8 presents various searching algorithms, a range including the very basic greedy search, branch-and-bound and improvements based on heuristics. I greatly appreciate the clear distinction between descriptive (chapter 9) and predictive (chapters 10 and 11) modeling. “Data Mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” (p. 1) As this quotation indicates, data sets are generally large. The data, therefore, has to be stored in databases so that the analysis required can be performed efficiently. Essentials in data organization for online analytical processing (OLAP) and databases are presented in chapter 12. The chapter also covers topics relevant to performance in databases such as b-trees, hash indices and multidimensional indices. The penultimate chapter (chapter 13) deals with how to find patterns in large data sets. A brief overview (chapter 14) of retrieval by content (text retrieval, image retrieval, sequence retrieval) concludes the book. I enjoyed the book, and believe it is an excellent choice for courses on data mining. The target group is clearly identified: students (or other people) interested in theoretical background, and definitely not expecting merely high-level business-oriented buzzwords. The first twelve chapters gradually build up basic knowledge. The author indicates which chapters can be skipped, depending on the reader’s focus. In my opinion, chapter 13 and 14 do not support the very clear reasoning of the rest of the book; they seem more as add-ons, - presenting relevant information. The print is clear and easy to read; colors, however, would sometimes be useful to help navigate the text. The physical book is of high quality; the paper and binding make it a durable textbook. That said, the book’s support for lecturers could be improved. To the best of my knowledge, there is no Web site that provides slides or other supporting material. A list of sample implementations of the presented algorithms would also be useful so that lecturers would not have to search for good demonstration programs themselves. Nonetheless, the book is one of the best textbooks I have seen on data mining, and truly earns my unqualified support. Online Computing Reviews Service