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008			220601s2019 sz | s |||| 0|eng d
020			_a9783031019135
024	7		_a10.1007/978-3-031-01913-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA174.7.D36 _b2019 EB
100	1		_aDong, Guozhu, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687233 _d1957-
245	1	0	_aExploiting the Power of Group Differences : _bUsing Patterns to Solve Data Analysis Problems _cby Guozhu Dong
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIII, 135 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Mining and Knowledge Discovery _x2151-0075
505	0		_aAcknowledgments -- Introduction and Overview -- General Preliminaries -- Emerging Patterns and a Flexible Mining Algorithm -- CAEP: Classification By Aggregating Multiple Matching Emerging Patterns -- CAEP for Classification on Tiny Training Datasets, Compound Selection, and Instance Selection -- OCLEP: One-Class Intrusion Detection and Anomaly Detection -- CPCQ: Contrast Pattern Based Clustering-Quality Evaluation -- CPC: Pattern-Based Clustering -- IBIG: Ranking Genes and Attributes for Complex Diseases and Complex Problems CPXR and CPXC: Pattern Aided Prediction Modeling and Prediction Model Analysis -- Other Approaches and Applications Using Emerging Patterns -- Bibliography -- Author's Biography -- Index.
520			_aThis book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9152614 _aReconocimiento de formas _xProceso de datos
650		7	_2embne _9405144 _aGrupos, Teoría de
776	0	8	_iPrinted edition: _z9783031001086
776	0	8	_iPrinted edition: _z9783031007859
776	0	8	_iPrinted edition: _z9783031030413
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01913-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI