05336nam 22006135 450 991029922760332120240131142351.03-319-14142-210.1007/978-3-319-14142-8(CKB)3710000000404016(SSID)ssj0001500983(PQKBManifestationID)11885068(PQKBTitleCode)TC0001500983(PQKBWorkID)11521984(PQKB)10842328(DE-He213)978-3-319-14142-8(MiAaPQ)EBC6310757(MiAaPQ)EBC5588258(Au-PeEL)EBL5588258(OCoLC)907922612(PPN)185484743(EXLCZ)99371000000040401620150413d2015 u| 0engurnn#mmmmamaatxtccrData Mining The Textbook /by Charu C. Aggarwal1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (XXIX, 734 p. 180 illus., 7 illus. in color.)Bibliographic Level Mode of Issuance: Monograph3-319-14141-4 Includes bibliographical references and index.Introduction to Data Mining -- Data Preparation -- Similarity and Distances -- Association Pattern Mining -- Association Pattern Mining: Advanced Concepts -- Cluster Analysis -- Cluster Analysis: Advanced Concepts -- Outlier Analysis -- Outlier Analysis: Advanced Concepts -- Data Classification -- Data Classification: Advanced Concepts -- Mining Data Streams -- Mining Text Data -- Mining Time-Series Data -- Mining Discrete Sequences -- Mining Spatial Data -- Mining Graph Data -- Mining Web Data -- Social Network Analysis -- Privacy-Preserving Data Mining.This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago.Data miningPattern perceptionData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XData mining.Pattern perception.Data Mining and Knowledge Discovery.Pattern Recognition.006.312Aggarwal Charu Cauthttp://id.loc.gov/vocabulary/relators/aut518673MiAaPQMiAaPQMiAaPQBOOK9910299227603321Data Mining2497825UNINA02867nam 2200433z- 450 991026114640332120210211(CKB)4100000002484628(oapen)https://directory.doabooks.org/handle/20.500.12854/43706(oapen)doab43706(EXLCZ)99410000000248462820202102d2016 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierComputational Methods for Understanding Complexity: The Use of Formal Methods in BiologyFrontiers Media SA20161 online resource (111 p.)Frontiers Research Topics2-88945-042-2 The complexity of living organisms surpasses our unaided habilities of analysis. Hence, computational and mathematical methods are necessary for increasing our understanding of biological systems. At the same time, there has been a phenomenal recent progress allowing the application of novel formal methods to new domains. This progress has spurred a conspicuous optimism in computational biology. This optimism, in turn, has promoted a rapid increase in collaboration between specialists of biology with specialists of computer science. Through sheer complexity, however, many important biological problems are at present intractable, and it is not clear whether we will ever be able to solve such problems. We are in the process of learning what kind of model and what kind of analysis and synthesis techniques to use for a particular problem. Some existing formalisms have been readily used in biological problems, others have been adapted to biological needs, and still others have been especially developed for biological systems. This Research Topic has examples of cases (1) employing existing methods, (2) adapting methods to biology, and (3) developing new methods. We can also see discrete and Boolean models, and the use of both simulators and model checkers. Synthesis is exemplified by manual and by machine-learning methods. We hope that the articles collected in this Research Topic will stimulate new research.Computational Methods for Understanding ComplexityGenetics (non-medical)bicsscAnswer set programingattractors of Boolean networksbiochemical networksBoolean networksGene Regulatory NetworksLogic programingmodel checkingsynthesis of biochemical modelsGenetics (non-medical)David A. Rosenbluethauth1331966BOOK9910261146403321Computational Methods for Understanding Complexity: The Use of Formal Methods in Biology3040714UNINA