Data Mining: A Knowledge Discovery Approach“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First, “long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second, to get “confession” from large data sets one needs to use state-of-the-art “torturing” tools. Third, Nature is very stubborn — not yielding easily or unwilling to reveal its secrets at all. Fortunately, while being aware of the above facts, the reader (a data miner) will find several efficient data mining tools described in this excellent book. The book discusses various issues connecting the whole spectrum of approaches, methods, techniques and algorithms falling under the umbrella of data mining. It starts with data understanding and preprocessing, then goes through a set of methods for supervised and unsupervised learning, and concludes with model assessment, data security and privacy issues. It is this specific approach of using the knowledge discovery process that makes this book a rare one indeed, and thus an indispensable addition to many other books on data mining. To be more precise, this is a book on knowledge discovery from data. As for the data sets, the easy-to-make statement is that there is no part of modern human activity left untouched by both the need and the desire to collect data. The consequence of such a state of affairs is obvious. |
Contents
9 | |
0CIOS03pdf | 26 |
0CIOS04pdf | 49 |
0CIOS05pdf | 69 |
0CIOS06pdf | 94 |
0CIOS07pdf | 133 |
0CIOS08pdf | 234 |
0CIOS09pdf | 255 |
0CIOS13pdf | 419 |
0CIOS14pdf | 453 |
0CIOS15pdf | 468 |
0CIOS16pdf | 487 |
0CIOSappApdf | 502 |
0CIOSappBpdf | 547 |
0CIOSappCpdf | 567 |
0CIOSappDpdf | 579 |
Other editions - View all
Data Mining: A Knowledge Discovery Approach Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Kurgan No preview available - 2010 |
Common terms and phrases
algorithm analysis association assume attributes basis calculate called Chapter classification clustering complex component computed concept conditional consider consists containing continuous corresponding covered criterion data mining data set database decision defined Definition denoted dependent described determine discovery discrete distance distribution documents domain elements equal equation error estimate evaluation event example feature selection Figure frequency function fuzzy given independent input intervals knowledge learning linear matrix mean measure methods minimal namely neuron normal Note objects obtained operations optimal original output parameters patterns performance points positive possible probability probability density problem projection query random variable reference regression relevant represented requires rules sample selection shown space specific square statistical step structure subset Table transaction transform tree values variable vector weights
Popular passages
Page 10 - Knowledge discovery in databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data [1].
Page 23 - Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.), Advances in Knowledge Discovery and Data Mining, pp.