Data mining theories, algorithms, and examples
New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehens...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press, an imprint of Taylor and Francis
2013.
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| Edición: | 1st edition |
| Colección: | Human factors and ergonomics.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629689706719 |
Tabla de Contenidos:
- Front Cover; Human Factors and Ergonomics Series; Published TiTles (conTinued); Contents; Preface; Overview of the Book; Distinctive Features of the Book; Teaching Support; Acknowledgments; Author; Part I: An Overview of Data Mining; Chapter 1: Introduction to Data, Data Patterns, and Data Mining; Part II: Algorithms for Mining Classification and Prediction Patterns; Chapter 2: Linear and Nonlinear Regression Models; Chapter 3: Naïve Bayes Classifier; Chapter 4: Decision and Regression Trees; Chapter 5: Artificial Neural Networks for Classification and Prediction
- Chapter 6: Support Vector MachinesChapter 7: k-Nearest Neighbor Classifier and Supervised Clustering; Part III: Algorithms for Mining Cluster and Association Patterns; Chapter 8: Hierarchical Clustering; Chapter 9: K-Means Clustering and Density-Based Clustering; Chapter 10: Self-Organizing Map; Chapter 11: Probability Distributions of Univariate Data; Chapter 12: Association Rules; Chapter 13: Bayesian Network; Part IV: Algorithms for Mining Data Reduction Patterns; Chapter 14: Principal Component Analysis; Chapter 15: Multidimensional Scaling
- Part V: Algorithms for Mining Outlier and Anomaly PatternsChapter 16: Univariate Control Charts; Chapter 17: Multivariate Control Charts; Part VI: Algorithms for Mining Sequential and Temporal Patterns; Chapter 18: Autocorrelation and Time Series Analysis; Chapter 19: Markov Chain Models and Hidden Markov Models; Chapter 20: Wavelet Analysis; References; Back Cover
