Advances in Data Science : Symbolic, Complex, and Network Data

Intro -- Table of Contents -- Preface -- Part 1: Symbolic Data -- 1 Explanatory Tools for Machine Learning in the Symbolic Data Analysis Framework -- 1.1. Introduction -- 1.2. Introduction to Symbolic Data Analysis -- 1.3. Symbolic data tables from Dynamic Clustering Method and EM -- 1.4. Criteria for ranking individuals, classes and their bar chart descriptive symbolic variables -- 1.5. Two directions of research -- 1.6. Conclusion -- 1.7. References -- 2 Likelihood in the Symbolic Context -- 2.1. Introduction -- 2.2. Probabilistic setting -- 2.3. Parametric models for p = 1 -- 2.4. Nonparametric estimation for p = 1 -- 2.5. Density models for p ≥ 2 -- 2.6. Conclusion -- 2.7. References -- 3 Dimension Reduction and Visualization of Symbolic Interval-Valued Data Using Sliced Inverse Regression -- 3.1. Introduction -- 3.2. PCA for interval-valued data and the sliced inverse regression -- 3.3. SIR for interval-valued data -- 3.4. Projections and visualization in DR subspace -- 3.5. Some computational issues -- 3.6. Simulation studies -- 3.7. A real data example: face recognition data -- 3.8. Conclusion and discussion -- 3.9. References -- 4 On the "Complexity" of Social Reality. Some Reflections About the Use of Symbolic Data Analysis in Social Sciences -- 4.1. Introduction -- 4.2. Social sciences facing "complexity" -- 4.3. Symbolic data analysis in the social sciences: an example -- 4.4. Conclusion -- 4.5. References -- Part 2: Complex Data -- 5 A Spatial Dependence Measure and Prediction of Georeferenced Data Streams Summarized by Histograms -- 5.1. Introduction -- 5.2. Processing setup -- 5.3. Main definitions -- 5.4. Online summarization of a data stream through CluStream for Histogram data -- 5.5. Spatial dependence monitoring: a variogram for histogram data -- 5.6. Ordinary kriging for histogram data -- 5.7. Experimental results on real data.