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A Divisive Clustering for Mixed Feature-Type Symbolic Data
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 Title & Authors
A Divisive Clustering for Mixed Feature-Type Symbolic Data
Kim, Jaejik;
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 Abstract
Nowadays we are considering and analyzing not only classical data expressed by points in the p-dimensional Euclidean space but also new types of data such as signals, functions, images, and shapes, etc. Symbolic data also can be considered as one of those new types of data. Symbolic data can have various formats such as intervals, histograms, lists, tables, distributions, models, and the like. Up to date, symbolic data studies have mainly focused on individual formats of symbolic data. In this study, it is extended into datasets with both histogram and multimodal-valued data and a divisive clustering method for the mixed feature-type symbolic data is introduced and it is applied to the analysis of industrial accident data.
 Keywords
mixed feature-type symbolic data;cluster analysis;industrial accident;
 Language
Korean
 Cited by
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