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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
15/06/2011 |
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Data da última atualização: |
25/07/2011 |
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Autoria: |
TAN, P.-N.; STEINBACH, M.; KUMAR, V. |
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Afiliação: |
PANG-NING TAN, Michigan State University; MICHAEL STEINBACH, University of Minnesota; VIPIN KUMAR, University of Minnesota. |
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Título: |
Introduction to data mining. |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
Boston: Addison Wesley, 2006. |
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Páginas: |
769 p. |
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ISBN: |
0-321-32136-7 |
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Idioma: |
Inglês |
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Conteúdo: |
What Is data mining? Motivating challenges. The origins of data mining. Data mining tasks. Scope and organization of the book. Bibliographic notes. Exercises. Data. Types of data. Data quality. Data preprocessing. Measures of similarity and dissimilarity. Exploring data. The Iris data set. Summary Statistics. Visualization. OLAP and multidimensional data analysis. Classification: basic concepts, decision trees, and model evaluation. Preliminaries. General approach to solving a classification problem. Decision tree induction. Model overfitting. Evaluating the performance of a classifier. Methods for comparing classifiers. Bibliographic notes. Exercises. Rule-based classifier. Nearest-neighbor classifiers. Bayesian Classifiers. Artificial Neural Network (ANN). Support Vector Machine (SVM). Ensemble methods. Class imbalance problem. Multiclass problem. Bibliographic notes. Exercises. Association analysis: basic concepts and algorithms. Problem definition. Frequent itemset generation. Rule generation. Compact representation of frequent itemsets. Alternative methods for generating frequent itemsets. FP-growth algorithm. Evaluation of association patterns.Effect of skewed support distribution. Bibliographic notes. Exercises. Handling categorical attributes. Handling continuous attributes. Handling a concept hierarchy. Sequential patterns. Subgraph patterns. Infrequent patterns. Bibliographic notes. Exercises. What Is cluster analysis? Different types of clusterings. Different types of clusters. K-means. Agglomerative hierarchical clustering. DBSCAN. Cluster evaluation. Bibliographic notes. Exercises. Characteristics of data, clusters, and clustering algorithms. Prototype-based clustering. Density-based clustering. Graph-based clustering. Scalable clustering algorithms. Which clustering algorithm? Bibliographic notes. Exercises. Causes of anomalies. Approaches to anomaly detection. The use of class labels. Issues. Statistical approaches. Proximity-based outlier detection. Density-based outlier detection. Clustering-based techniques. Bibliographic notes. Exercises.
Linear Algebra. Dimensionality reduction. Probability and statistics. Regression. Optimization. MenosWhat Is data mining? Motivating challenges. The origins of data mining. Data mining tasks. Scope and organization of the book. Bibliographic notes. Exercises. Data. Types of data. Data quality. Data preprocessing. Measures of similarity and dissimilarity. Exploring data. The Iris data set. Summary Statistics. Visualization. OLAP and multidimensional data analysis. Classification: basic concepts, decision trees, and model evaluation. Preliminaries. General approach to solving a classification problem. Decision tree induction. Model overfitting. Evaluating the performance of a classifier. Methods for comparing classifiers. Bibliographic notes. Exercises. Rule-based classifier. Nearest-neighbor classifiers. Bayesian Classifiers. Artificial Neural Network (ANN). Support Vector Machine (SVM). Ensemble methods. Class imbalance problem. Multiclass problem. Bibliographic notes. Exercises. Association analysis: basic concepts and algorithms. Problem definition. Frequent itemset generation. Rule generation. Compact representation of frequent itemsets. Alternative methods for generating frequent itemsets. FP-growth algorithm. Evaluation of association patterns.Effect of skewed support distribution. Bibliographic notes. Exercises. Handling categorical attributes. Handling continuous attributes. Handling a concept hierarchy. Sequential patterns. Subgraph patterns. Infrequent patterns. Bibliographic notes. Exercises. What Is cluster analysis? Different types of clusterings. Different ty... Mostrar Tudo |
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Palavras-Chave: |
Clusterização; Inteligência artificial; Mineração de dados; Recuperação da informação. |
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Thesaurus Nal: |
Artificial intelligence; Cluster analysis; Information retrieval. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02824nam a2200241 a 4500
001 1891508
005 2011-07-25
008 2006 bl uuuu 00u1 u #d
020 $a0-321-32136-7
100 1 $aTAN, P.-N.
245 $aIntroduction to data mining.
260 $aBoston: Addison Wesley$c2006
300 $a769 p.
520 $aWhat Is data mining? Motivating challenges. The origins of data mining. Data mining tasks. Scope and organization of the book. Bibliographic notes. Exercises. Data. Types of data. Data quality. Data preprocessing. Measures of similarity and dissimilarity. Exploring data. The Iris data set. Summary Statistics. Visualization. OLAP and multidimensional data analysis. Classification: basic concepts, decision trees, and model evaluation. Preliminaries. General approach to solving a classification problem. Decision tree induction. Model overfitting. Evaluating the performance of a classifier. Methods for comparing classifiers. Bibliographic notes. Exercises. Rule-based classifier. Nearest-neighbor classifiers. Bayesian Classifiers. Artificial Neural Network (ANN). Support Vector Machine (SVM). Ensemble methods. Class imbalance problem. Multiclass problem. Bibliographic notes. Exercises. Association analysis: basic concepts and algorithms. Problem definition. Frequent itemset generation. Rule generation. Compact representation of frequent itemsets. Alternative methods for generating frequent itemsets. FP-growth algorithm. Evaluation of association patterns.Effect of skewed support distribution. Bibliographic notes. Exercises. Handling categorical attributes. Handling continuous attributes. Handling a concept hierarchy. Sequential patterns. Subgraph patterns. Infrequent patterns. Bibliographic notes. Exercises. What Is cluster analysis? Different types of clusterings. Different types of clusters. K-means. Agglomerative hierarchical clustering. DBSCAN. Cluster evaluation. Bibliographic notes. Exercises. Characteristics of data, clusters, and clustering algorithms. Prototype-based clustering. Density-based clustering. Graph-based clustering. Scalable clustering algorithms. Which clustering algorithm? Bibliographic notes. Exercises. Causes of anomalies. Approaches to anomaly detection. The use of class labels. Issues. Statistical approaches. Proximity-based outlier detection. Density-based outlier detection. Clustering-based techniques. Bibliographic notes. Exercises. Linear Algebra. Dimensionality reduction. Probability and statistics. Regression. Optimization.
650 $aArtificial intelligence
650 $aCluster analysis
650 $aInformation retrieval
653 $aClusterização
653 $aInteligência artificial
653 $aMineração de dados
653 $aRecuperação da informação
700 1 $aSTEINBACH, M.
700 1 $aKUMAR, V.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 1 | |
| 1. |  | POTTER, C.; KLOOSTER, S.; STEINBACH, M.; TAN, P.-N.; KUMAR, V.; SHEKHAR, S.; CARVALHO, C. R. de. Understanding global teleconnections of climate to regional model estimates of Amazon ecosystem carbon fluxes. Global Change Biology, v. 10, n. 5, p. 693-703, May 2004.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: Internacional - A |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 1 | |
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