Core concepts in data analysis: summarization, correlation and visualization

Core Concepts in Data Analysis: Summarization, Correlation and Visualization provides in-depth descriptions of those data analysis approaches that either summarize data (principal component analysis and clustering, including hierarchical and network clustering) or correlate different aspects of data (decision trees, linear rules, neuron networks, and Bayes rule). Boris Mirkin takes an unconventional approach exercises are provided: worked examples, case studies, projects and questions. Provides an in-depth understanding of a few basic techniques in data analysis rather than covering the broad spectrum of approaches developed to date. Explores methodical innovations of summarization and correlation techniques in a cognitive way. Includes worked examples, case studies, projects and questions, ideal for class and self-study. INDICE: Introduction.-1D Analysis: Summarization and Visualisation of a Single Feature.-2D Analysis: Correlation and Visualition of Two Features.-Learning Multivariate Correlations in Data.-Principal Component Analysis and SVD.-K-Means and Related Clustering Methods.-Hierarchial Clustering.-Approximate and Spectral Clustering for Network and Affinity Data.

• ISBN: 978-0-85729-286-5
• Editorial: Springer London
• Encuadernacion: Rústica
• Páginas: 428
• Fecha Publicación: 01/03/2011
• Nº Volúmenes: 1
• Idioma: Inglés