tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 102

Data Mining and Knowledge Discovery Handbook, 2 Edition part 102. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 990 Lior Rokach and Oded Maimon Berry and Linoff 2000 state that decomposition can be also useful for handling missing data. In this case they do not refer to sporadic missing data but to the case where several attribute values are available for some tuples but not for all of them. For instance Historical data such as billing information is available only for customers who have been around for a sufficiently long time or Outside data such as demographics is available only for the subset of the customer base that matches . In this case one classifier can be trained for customers having all the information and a second classifier for the remaining customers. The Mutually Exclusive Property This property indicates whether the decomposition is mutually exclusive disjointed decomposition or partially overlapping . a certain value of a certain attribute in a certain tuple is utilized more than once . For instance in the case of sample decomposition mutually exclusive means that a certain tuple cannot belong to more than one subset Domingos 1996 Chan and Stolfo 1995 . Bay 1999 on the other hand has used non-exclusive feature decomposition. Similarly CART and MARS perform mutually exclusive decomposition of the input space while HME allows sub-spaces to overlap. Mutually exclusive decomposition can be deemed as a pure decomposition. While pure decomposition forms a restriction on the problem space it has some important and helpful properties A greater tendency in reduction of execution time than non-exclusive approaches. Since most learning algorithms have computational complexity that is greater than linear in the number of attributes or tuples partitioning the problem dimensionality in a mutually exclusive manner means a decrease in computational complexity Provost and Kolluri 1997 . Since mutual exclusiveness entails using smaller datasets the models obtained for each sub-problem are smaller in size. Without the mutually exclusive restriction each model can be