Verfügbarkeit: Principles of data mining | Technische Universität München

Principles of data mining:

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Bibliographische Detailangaben
Beteilige Person:	Bramer, Max A. 1948- (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	Englisch
Veröffentlicht:	London [u.a.] Springer 2013
Ausgabe:	2. ed.
Schriftenreihe:	Undergraduate topics in computer science
Schlagwörter:	Data mining Data Mining Lehrbuch
Links:	https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 https://doi.org/10.1007/978-1-4471-4884-5 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025895002&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025895002&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Includes bibliographical references and index
Umfang:	1 Online-Ressource
ISBN:	9781447148838 9781447148845
DOI:	10.1007/978-1-4471-4884-5

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Datensatz im Suchindex

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adam_text	PRINCIPLES OF DATA MINING / BRAMER, MAX : 2013 TABLE OF CONTENTS / INHALTSVERZEICHNIS INTRODUCTION TO DATA MINING DATA FOR DATA MINING INTRODUCTION TO CLASSIFICATION: NAIVE 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 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 DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT. PRINCIPLES OF DATA MINING / BRAMER, MAX : 2013 ABSTRACT / INHALTSTEXT DATA MINING, THE AUTOMATIC EXTRACTION OF IMPLICIT AND POTENTIALLY USEFUL INFORMATION FROM DATA, IS INCREASINGLY USED IN COMMERCIAL, SCIENTIFIC AND OTHER APPLICATION AREAS. PRINCIPLES OF DATA MINING EXPLAINS AND EXPLORES THE PRINCIPAL TECHNIQUES OF DATA MINING: FOR CLASSIFICATION, ASSOCIATION RULE MINING AND CLUSTERING. EACH TOPIC IS CLEARLY EXPLAINED AND ILLUSTRATED BY DETAILED WORKED EXAMPLES, WITH A FOCUS ON ALGORITHMS RATHER THAN MATHEMATICAL FORMALISM. IT IS WRITTEN FOR READERS WITHOUT A STRONG BACKGROUND IN MATHEMATICS OR STATISTICS, AND ANY FORMULAE USED ARE EXPLAINED IN DETAIL. THIS SECOND EDITION HAS BEEN EXPANDED TO INCLUDE ADDITIONAL CHAPTERS ON USING FREQUENT PATTERN TREES FOR ASSOCIATION RULE MINING, COMPARING CLASSIFIERS, ENSEMBLE CLASSIFICATION AND DEALING WITH VERY LARGE VOLUMES OF DATA. PRINCIPLES OF DATA MINING 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. SUITABLE 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 DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
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title	Principles of data mining
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