Exploratory Data Analysis of Interval-valued Symbolic Data with Matrix Visualization and Related Wor

Abstract

Abstract??? Symbolic data analysis (SDA) has gained popularity over the past few years because of its potential for handling data having a dependent and hierarchical nature. Interval-valued data is by far the most popular symbolic data type in the literature and the most commonly encountered one in practice. Matrix visualization (MV) techniques such as GAP (generalized association plots) are useful exploratory data analysis tools for visualizing and clustering high-dimensional data structure. In this presentation I will summarize our development on matrix visualization for interval-valued symbolic data with examples. Moreover I will talk about our ongoing work on interval-valued data analysis.