Data Mining and Machine Learning: Fundamental Concepts and AlgorithmsCambridge University Press, 2020 M01 30 - 776 páginas The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning. |
Contenido
Numeric Attributes | 29 |
Categorical Attributes | 61 |
Graph Data | 92 |
Kernel Methods | 134 |
Highdimensional Data | 163 |
Dimensionality Reduction | 184 |
FREQUENT PATTERN MINING | 217 |
Summarizing Itemsets | 244 |
Spectral and Graph Clustering | 394 |
Clustering Validation | 426 |
CLASSIFICATION | 467 |
Decision Tree Classifier | 483 |
Linear Discriminant Analysis | 501 |
Support Vector Machines | 517 |
Classification Assessment | 546 |
REGRESSION | 587 |
Sequence Mining | 261 |
Graph Pattern Mining | 282 |
Pattern and Rule Assessment | 303 |
CLUSTERING | 332 |
Hierarchical Clustering | 364 |
Densitybased Clustering | 375 |
Logistic Regression | 623 |
Neural Networks | 637 |
Deep Learning | 672 |
Regression Evaluation | 720 |
| 755 | |
Otras ediciones - Ver todas
Data Mining and Machine Learning: Fundamental Concepts and Algorithms Mohammed J. Zaki,Wagner Meira, Jr Vista previa limitada - 2019 |
Data Mining and Machine Learning: Fundamental Concepts and Algorithms Mohammed J. Zaki,Wagner Meira, Jr Vista previa limitada - 2020 |
Términos y frases comunes
activation Algorithm approach assume attributes average bias called centered classifier cluster coefficient column components comprising compute connected Consider corresponding covariance matrix database dataset defined denote density derivative dimensions distance distribution edges eigenvalues eigenvector error estimate Example expected extensions fact feature space Figure Finally follows function Further given gradient graph hyperplane implies independent initial input interval Iris kernel label layer learning length linear mapping matrix maximal mean measures method mining neurons node normal Note objective observed obtain output pairs parameters partition points positive possible predicted probability projected random variable regression respect response rule sample sepal sequence shown in Figure shows split squared standard statistic step suffix symbol Table takes tree true update variance vector weight zero