000			03895nam a22003857a 4500
003			OSt
005			20250707162827.0
008			250707b |||||||| |||| 00| 0 eng d
020			_a9783642010873 _q(hardcover)
040			_beng _cDLC _dDLC _erda _aSai University Library
082			_222 _a006.3 _bABR
245			_aFoundations of computational intelligence _cAjith Abraham, Aboul-Ella Hassanien, André Ponce de Leon F. Carvalho editors _nVolume 4, _pBio-inspired data mining /
260			_aBerlin ; _bSpringer, _c2009
264		1	_aBerlin ; _bSpringer, _c2009
264		4	_c©2009
300			_axiii, 394 pages : _billustrations ; _c25 cm
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
490	1		_aStudies in computational intelligence ; _vv. 204
500			_aGifted by Professor Ajith Abraham
505			_aIncludes bibliographical references and index
520			_aRecent advances in the computing and electronics technology, particularly in sensor devices, databases and distributed systems, are leading to an exponential growth in the amount of data stored in databases. It has been estimated that this amount doubles every 20 years. For some applications, this increase is even steeper. Databases storing DNA sequence, for example, are doubling their size every 10 months. This growth is occurring in several applications areas besides bioinformatics, like financial transactions, government data, environmental monitoring, satellite and medical images, security data and web. As large organizations recognize the high value of data stored in their databases and the importance of their data collection to support decision-making, there is a clear demand for sophisticated Data Mining tools. Data mining tools play a key role in the extraction of useful knowledge from databases. They can be used either to confirm a particular hypothesis or to automatically find patterns. In the second case, which is related to this book, the goal may be either to describe the main patterns present in dataset, what is known as descriptive Data Mining or to find patterns able to predict behaviour of specific attributes or features, known as predictive Data Mining. While the first goal is associated with tasks like clustering, summarization and association, the second is found in classification and regression problems. Computational tools or solutions based on intelligent systems are being used with great success in Data Mining applications. Nature has been very successful in providing clever and efficient solutions to different sorts of challenges and problems posed to organisms by ever-changing and unpredictable environments. It is easy to observe that strong scientific advances have been made when issues from different research areas are integrated. A particularly fertile integration combines biology and computing. Computational tools inspired on biological process can be found in a large number of applications. One of these applications is Data Mining, where computing techniques inspired on nervous systems; swarms, genetics, natural selection, immune systems and molecular biology have provided new efficient alternatives to obtain new, valid, meaningful and useful patterns in large datasets. This Volume comprises of 16 chapters, including an overview chapter, providing an up-to-date and state-of-the research on the application of Bio-inspired techniques for Data Mining
650		1	_aNatural computation
650		1	_aData mining
650		1	_aBioinformatics
650		1	_aComputational biology
650		1	_aArtificial intelligence
650		1	_aEngineering
700	1		_aAbraham, Ajith _eeditor
700	1		_aHassanien, Aboul-Ella _eeditor
700	1		_aCarvalho, André Ponce de Leon F. de _eeditor
830			_aStudies in computational intelligence ; _vv. 204
942			_2ddc _cBK
999			_c6838 _d6838