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001 978-3-031-01913-5
003 DE-He213
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008 220601s2019 sz | s |||| 0|eng d
020 _a9783031019135
_9978-3-031-01913-5
024 7 _a10.1007/978-3-031-01913-5
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aDong, Guozhu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980431
245 1 0 _aExploiting the Power of Group Differences
_h[electronic resource] :
_bUsing Patterns to Solve Data Analysis Problems /
_cby Guozhu Dong.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIII, 135 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_980432
650 2 4 _aStatistics.
_914134
710 2 _aSpringerLink (Online service)
_980433
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001086
776 0 8 _iPrinted edition:
_z9783031007859
776 0 8 _iPrinted edition:
_z9783031030413
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_980434
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01913-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84960
_d84960