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008			170103s2016 sz o 101 0 eng d
020			_a3319512811 _q(electronic bk.)
020			_a9783319512815 _q(electronic bk.)
020			_z9783319512792 _q(print)
035			_a(OCoLC)967512045
040			_aN$T _cN$T _dIDEBK _dEBLCP _dGW5XE _dN$T _dYDX _dOCLCF _dCOO _dOCLCQ _dUAB _dES-MaUEC _bspa
050		4	_aQ342 _b.R434 2016 EB
111	2		_aInternational Conference on Soft Computing and Data Mining _n(2nd : _d2016 : _cBandung, Indonesia)
245	1	0	_aRecent advances on soft computing and data mining : _bthe second International Conference on Soft Computing and Data Mining (SCDM-2016), Bandung, Indonesia, August 18-20, 2016 Proceedings _cTutut Herawan, Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, editors.
246	3		_aSCDM 216
264		1	_aCham, Switzerland _bSpringer _c2017
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in intelligent systems and computing _x2194-5357 _vvolume 549
500			_aIncluye índice
500			_aInternational conference proceedings.
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aPreface; Conference Organization; Patron; Honorary Chairs; Steering Committee; General Chairs; Organizing Committee; Program Committee Chairs; Proceeding Chairs; Workshop Chairs; Sponsorship; Program Committee ; Soft Computing; Data Mining; Workshop on Ensemble Methods and Their Applications; Workshop on Web Mining, Services and Security; Contents; Soft Computing; Cluster Validation Analysis on Attribute Relative of Soft-Set Theory; 1 Introduction; 2 Preliminaries; 2.1 Soft-Set Theory; 2.2 Clustering Attribute Selection; 2.3 Cluster Validation Method; 3 Attribute Relative of Soft-Set Theory
505	8		_a4 Result and Discussion5 Conclusions; References; Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithm; Abstract; 1 Introduction; 2 Wolf Search (WS) Algorithm; 2.1 The Proposed WRNN Algorithm; 3 Results and Discussion; 3.1 Preliminary Study; 3.2 Classification Datasets; 4 Conclusion; Acknowledgments; References; Optimization of ANFIS Using Artificial Bee Colony Algorithm for Classification of Malaysian SMEs; 1 Introduction; 2 The ANFIS Concept; 3 Artificial Bee Colony Algorithm; 4 Methodology; 4.1 ANFIS Rule-Base Minimization and Accuracy Maximization Using ABC
505	8		_a5 ConclusionReferences; Formation Control Optimization for Odor Localization; Abstract; 1 Introduction; 2 Odor Localization Design; 2.1 Odor Localization Process; 2.2 The Localization Process-Based FKN-PSO Algorithm; 3 Experimental Results; 4 Conclusion and Future Work; Acknowledgments; References; A New Search Direction for Broyden's Family Method in Solving Unconstrained Optimization Problems; Abstract; 1 Introduction; 2 Preliminaries; 3 Proposed Model; 4 Experimental Result; 5 Conclusion; References
505	8		_a5 Simulation Results and Analysis6 Conclusion; References; Forecasting of Malaysian Oil Production and Oil Consumption Using Fuzzy Time Series; Abstract; 1 Introduction; 2 The Basic Theories of FTS and RTS; 2.1 FTS Theory and Its Procedure; 2.2 RTS Theory and Its Procedure; 3 Proposed Interval Adjustment and Algorithm in FTS; 4 Empirical Analysis; 5 Conclusion; Acknowledgment; References; A Fuzzy TOPSIS with Z-Numbers Approach for Evaluation on Accident at the Construction Site; Abstract; 1 Introduction; 2 The Steps of Z-Numbers with Fuzzy TOPSIS; 3 An Application; 4 Comparative Studies
505	8		_aImproved Functional Link Neural Network Learning Using Modified Bee-Firefly Algorithm for Classification TaskAbstract; 1 Introduction; 2 Functional Link Neural Network Learning Scheme; 3 Modified Artificial Bee Colony; 4 Firefly Algorithm; 5 Modified Bee-Firefly Algorithm; 6 Experimentation and Result; 7 Conclusion; Acknowledgments; References; Artificial Neural Network with Hyperbolic Tangent Activation Function to Improve the Accuracy of COCOMO II Model; Abstract; 1 Introduction; 2 Related Works; 3 Rudimentary; 3.1 COCOMO II Model; 3.2 Artificial Neural Network; 3.2.1 Identity Function
520	3		_aThis book provides a comprehensive introduction and practical look at the concepts and techniques readers need to get the most out of their data in real-world, large-scale data mining projects. It also guides readers through the data-analytic thinking necessary for extracting useful knowledge and business value from the data. The book is based on the Soft Computing and Data Mining (SCDM-16) conference, which was held in Bandung, Indonesia on August 18th?20th 2016 to discuss the state of the art in soft computing techniques, and offer participants sufficient knowledge to tackle a wide range of complex systems. The scope of the conference is reflected in the book, which presents a balance of soft computing techniques and data mining approaches. The two constituents are introduced to the reader systematically and brought together using different combinations of applications and practices. It offers engineers, data analysts, practitioners, scientists and managers the insights into the concepts, tools and techniques employed, and as such enables them to better understand the design choice and options of soft computing techniques and data mining approaches that are necessary to thrive in this data-driven ecosystem.
650		7	_aData mining _2embne _0(OCoLC)fst00887946 _0comprobar BNE20033218554 _9162648
700	1		_aDeris, Mustafa Mat, _eeditor literario
700	1		_aGhazali, Rozaida, _eeditor literario
700	1		_aHerawan, Tutut, _eeditor literario
700	1		_aNawi, Nazri Mohd, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-51281-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95125 _d95125 _x1