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- Wiley
More About This Title Clustering Methodology for Symbolic Data
English
Classification Methodology for Symbolic Data:
- Provides new classification methodologies for histogram valued data reaching across many fields in data science.
- Demonstrates how to manage a large complex dataset into manageable datasets ready for analysis.
- Features very large contemporary datasets such as time series, interval-valued data and histogram-valued data
- Considers classification models such as dynamical clustering, an extension of K-means, hierarchical pyramidal and Kohonen methodology in detail.
- Includes principal components and correspondence analysis methodology.
- Features a supporting website hosting relevant software.
- Edwin Diday is the founding father of Symbolic Data Analysis.
- Extends and expands on the material in Symbolic Data Analysis: Conceptual Statistics and Data Mining, Billard and Diday (2006)
Classification Methodology for Symbolic Datais aimed at the practitioners of symbolic data analysis: statisticians and economists within the public (e.g. national statistics institutes) and private (e.g. banks, insurance companies, companies managing databases) sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bio-engineering.
English
Lynne Billard, PhD, is University Professor in the Department of Statistics at the University of Georgia, USA. She has over 225 publications mostly in leading journals, and co-edited six books. Professor Billard was a former president of ASA, IBS, and ENAR.
Edwin Diday, PhD is Professor of Computer Science at Centre De Recherche en Mathematiques de la Decision, Universite Paris 9, France. He has published fifty-eight papers and authored or edited fourteen books. Professor Diday is also the founder of the Symbolic Data Analysis field.
English
Chapter 1 Introduction
Chapter 2 Symbolic Data – Basics
Chapter 3 Dissimilarity, Similarity, and Distance Measures
Chapter 4 Dissimilarity, Similarity, and Distance Measures - Modal Data
Chapter 5 General Clustering Techniques
Chapter 6 Partitioning Techniques
Chapter 7 Divisive Hierarchical Clustering
Chapter 8 Agglomerative Hierarchical Clustering
