find Hier klicken Jetzt informieren PR CR0917 Cloud Drive Photos Alles für die Schule Learn More TDZ HI_PROJECT Mehr dazu Mehr dazu Shop Kindle AlexaundMusic AmazonMusicUnlimited Fußball longss17


oder
Loggen Sie sich ein, um 1-Click® einzuschalten.
Alle Angebote
Möchten Sie verkaufen? Hier verkaufen
Jeder kann Kindle Bücher lesen — selbst ohne ein Kindle-Gerät — mit der KOSTENFREIEN Kindle App für Smartphones, Tablets und Computer.

Introduction to Data Mining [Englisch] [Taschenbuch]

Pang-Ning Tan , Michael Steinbach , Vipin Kumar
4.0 von 5 Sternen  Alle Rezensionen anzeigen (2 Kundenrezensionen)
Statt: EUR 80,95
Jetzt: EUR 70,99 GRATIS Lieferung innerhalb Deutschlands Siehe Details.
Sie sparen: EUR 9,96 (12%)
  Alle Preisangaben inkl. MwSt.
o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o
Nur noch 8 auf Lager (mehr ist unterwegs).
Verkauf und Versand durch Amazon. Geschenkverpackung verfügbar.
Lieferung bis Mittwoch, 27. September: Siehe Details.
‹  Zurück zur Artikelübersicht

Inhaltsverzeichnis

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 9 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 10 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 11 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 B Dimensionality ReductionAppendix D Regression Appendix E Optimization Author IndexSubject Index

‹  Zurück zur Artikelübersicht