LEADER 05176nam 22006615 450 001 9910409668803321 005 20251114111232.0 010 $a1-4471-7493-3 024 7 $a10.1007/978-1-4471-7493-6 035 $a(CKB)4100000011254380 035 $a(MiAaPQ)EBC6207654 035 $a(DE-He213)978-1-4471-7493-6 035 $a(MiAaPQ)EBC6420168 035 $a(Au-PeEL)EBL6420168 035 $a(OCoLC)1155482662 035 $a(PPN)248395270 035 $a(EXLCZ)994100000011254380 100 $a20200520d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPrinciples of Data Mining /$fby Max Bramer 205 $a4th ed. 2020. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2020. 215 $a1 online resource (576 pages) 225 1 $aUndergraduate Topics in Computer Science,$x1863-7310 311 08$a1-4471-7492-5 320 $aIncludes bibliographical references and index. 327 $aIntroduction to Data Mining -- Data for Data Mining -- Introduction to Classification: Naïve Bayes and Nearest Neighbour -- Using Decision Trees for Classification -- Decision Tree Induction: Using Entropy for Attribute Selection -- Decision Tree Induction: Using Frequency Tables for Attribute Selection -- Estimating the Predictive Accuracy of a Classifier -- Continuous Attributes -- Avoiding Overfitting of Decision Trees -- More About Entropy -- Inducing Modular Rules for Classification -- Measuring the Performance of a Classifier -- Dealing with Large Volumes of Data -- Ensemble Classification -- Comparing Classifiers -- Associate Rule Mining I -- Associate Rule Mining II -- Associate Rule Mining III -- Clustering -- Mining -- Classifying Streaming Data -- Classifying Streaming Data II: Time-dependent Data -- An Introduction to Neural Networks -- Appendix A ? Essential Mathematics -- Appendix B ? Datasets -- Appendix C ? Sources of Further Information -- Appendix D ? Glossary and Notation -- Appendix E ? Solutions to Self-assessment Exercises -- Index. 330 $aThis book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self-study, it aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. Principles of Data Mining includes descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift. The expanded fourth edition gives a detailed description of a feed-forward neural network with backpropagation and shows how it can be used for classification. 410 0$aUndergraduate Topics in Computer Science,$x1863-7310 606 $aInformation storage and retrieval 606 $aDatabase management 606 $aArtificial intelligence 606 $aComputer programming 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 615 0$aInformation storage and retrieval. 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 14$aInformation Storage and Retrieval. 615 24$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 676 $a006.312 700 $aBramer$b Max$4aut$4http://id.loc.gov/vocabulary/relators/aut$0849832 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409668803321 996 $aPrinciples of Data Mining$91897503 997 $aUNINA