Data Mining - Mehmed Kantardzic [3]
The new discipline of data mining has developed especially to extract valuable information from such huge data sets. In recent years there has been an explosive growth of methods for discovering new knowledge from raw data. This is not surprising given the proliferation of low-cost computers (for implementing such methods in software), low-cost sensors, communications, and database technology (for collecting and storing data), and highly computer-literate application experts who can pose “interesting” and “useful” application problems.
Data-mining technology is currently a hot favorite in the hands of decision makers as it can provide valuable hidden business and scientific “intelligence” from large amount of historical data. It should be remembered, however, that fundamentally, data mining is not a new technology. The concept of extracting information and knowledge discovery from recorded data is a well-established concept in scientific and medical studies. What is new is the convergence of several disciplines and corresponding technologies that have created a unique opportunity for data mining in scientific and corporate world.
The origin of this book was a wish to have a single introductory source to which we could direct students, rather than having to direct them to multiple sources. However, it soon became apparent that a wide interest existed, and potential readers other than our students would appreciate a compilation of some of the most important methods, tools, and algorithms in data mining. Such readers include people from a wide variety of backgrounds and positions, who find themselves confronted by the need to make sense of large amount of raw data. This book can be used by a wide range of readers, from students wishing to learn about basic processes and techniques in data mining to analysts and programmers who will be engaged directly in interdisciplinary teams for selected data mining applications. This book reviews state-of-the-art techniques for analyzing enormous quantities of raw data in a high-dimensional data spaces to extract new information useful in decision-making processes. Most of the definitions, classifications, and explanations of the techniques covered in this book are not new, and they are presented in references at the end of the book. One of the author’s main goals was to concentrate on a systematic and balanced approach to all phases of a data mining process, and present them with sufficient illustrative examples. We expect that carefully prepared examples should give the reader additional arguments and guidelines in the selection and structuring of techniques and tools for his or her own data mining applications. A better understanding of the implementational details for most of the introduced techniques will help challenge the reader to build his or her own tools or to improve applied methods and techniques.
Teaching in data mining has to have emphasis on the concepts and properties of the applied methods, rather than on the mechanical details of how to apply different data mining tools. Despite all of their attractive “bells and whistles,” computer-based tools alone will never provide the entire solution. There will always be the need for the practitioner to make important decisions regarding how the whole process will be designed, and how and which tools will be employed. Obtaining a deeper understanding of the methods and models, how they behave, and why they behave the way they do is a prerequisite for efficient and successful application of data mining technology. The premise of this book is that there are just a handful of important principles and issues in the field of data mining. Any researcher or practitioner in this field needs to be aware of these issues in order to successfully