Data mining : the textbook / Charu C. Aggarwal
Tipo de material:
TextoProductor: New York, NY : Distribuidor: Springer, Fecha de copyright: ©2015Edición: 1a ediciónDescripción: xxix, 734 páginas : ilustraciones, gráficas ; 25 x 19 cmTipo de contenido: - texto
- sin medio
- volumen
- 9783319141411
- QA 76 .9 .D343 A26 2015
| Tipo de ítem | Biblioteca actual | Biblioteca de origen | Colección | Signatura topográfica | Copia número | Estado | Notas | Fecha de vencimiento | Código de barras | Reserva de ítems | |
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Libros para consulta en sala
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Biblioteca Antonio Enriquez Savignac | Biblioteca Antonio Enriquez Savignac | COLECCIÓN RESERVA | QA 76 .9 .D343 A26 2015 (Navegar estantería(Abre debajo)) | 1 | No para préstamo | Ingeniería en Datos e Inteligencia Organizacional | 040444 |
Incluye bibliografía: páginas 695-726
An Introduction to Data Mining -- Data Preparation -- Similarity and Distances -- Association Pattern Mining -- Association Pattern Mining: Advanced Concepts -- Cluster Analysis -- Cluster Analysis: Advanced Concepts -- Outlier Analysis -- Outlier Analysis: Advanced Concepts -- Data Classification -- Data Classification: Advanced Concepts -- Mining Data Streams -- Mining Text Data -- Mining Time Series Data -- Mining Discrete Sequences -- Mining Spatial Data -- Mining Graph Data -- Mining Web Data -- Social Network Analysis -- Privacy-Preserving Data Mining
"This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples." -- P. web editorial
Ingeniería de Datos e Intelegiencia
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