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Introduction to data mining

Author: Pang-Ning Tan; Vipin Kumar; Michael Steinbach
Publisher: Boston, Mass. : Pearson/Addison-Wesley, 2006.
Series: Pearson international Edition
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique,  Read more...
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Genre/Form: Einführung
Einführung
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Pang-Ning Tan; Vipin Kumar; Michael Steinbach
ISBN: 0321321367 9780321321367 9780321420527 0321420527
OCLC Number: 254242932
Description: XXI, 769 Seiten : Illustrationen, Diagramme.
Contents: 1 Introduction1.1 What is Data Mining? 1.2 Motivating Challenges1.3 The Origins of Data Mining1.4 Data Mining Tasks1.5 Scope and Organization of the Book 1.6 Bibliographic Notes1.7 Exercises 2 Data 2.1 Types of Data 2.2 Data Quality 2.3 Data Preprocessing 2.4 Measures of Similarity and Dissimilarity 2.5 Bibliographic Notes 2.6 Exercises 3 Exploring Data 3.1 The Iris Data Set 3.2 Summary Statistics 3.3 Visualization 3.4 OLAP and Multidimensional Data Analysis3.5 Bibliographic Notes3.6 Exercises 4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries 4.2 General Approach to Solving a Classification Problem 4.3 Decision Tree Induction 4.4 Model Overfitting4.5 Evaluating the Performance of a Classifier4.6 Methods for Comparing Classifiers4.7 Bibliographic Notes 4.8 Exercises 5 Classification: Alternative Techniques 5.1 Rule-Based Classifier 5.2 Nearest-Neighbor Classifiers5.3 Bayesian Classifiers 5.4 Artificial Neural Network (ANN) 5.5 Support Vector Machine (SVM) 5.6 Ensemble Methods 5.7 Class Imbalance Problem5.8 Multiclass Problem5.9 Bibliographic Notes5.10 Exercises 6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition 6.2 Frequent Itemset Generation 6.3 Rule Generation 6.4 Compact Representation of Frequent Itemsets6.5 Alternative Methods for Generating Frequent Itemsets6.6 FP-Growth Algorithm 6.7 Evaluation of Association Patterns 6.8 Effect of Skewed Support Distribution6.9 Bibliographic Notes 6.10 Exercises 7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes 7.2 Handling Continuous Attributes 7.3 Handling a Concept Hierarchy 7.4 Sequential Patterns 7.5 Subgraph Patterns 7.6 Infrequent Patterns 7.7 Bibliographic Notes 7.8 Exercises 8 Cluster Analysis: Basic Concepts and Algorithms 8.1 Overview 8.2 K-means 8.3 Agglomerative Hierarchical Clustering 8.4 DBSCAN 8.5 Cluster Evaluation 8.6 Bibliographic Notes 8.7 Exercises 9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms9.2 Prototype-Based Clustering 9.3 Density-Based Clustering 9.4 Graph-Based Clustering 9.5 Scalable Clustering Algorithms 9.6 Which Clustering Algorithm? 9.7 Bibliographic Notes 9.8 Exercises 10 Anomaly Detection 10.1 Preliminaries10.2 Statistical Approaches10.3 Proximity-Based Outlier Detection10.4 Density-Based Outlier Detection10.5 Clustering-Based Techniques10.6 Bibliographic Notes10.7 Exercises Appendix A Linear Algebra Appendix B Dimensionality ReductionAppendix C Probability and Statistics Appendix D Regression Appendix E Optimization Author IndexSubject Index
Series Title: Pearson international Edition
Responsibility: Pang-Ning Tan ; Michael Steinbach ; Vipin Kumar.
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Abstract:

Presents fundamental concepts and algorithms for those learning data mining for the first time. This book explores each concept and features each major topic organized into two chapters, beginning  Read more...

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