Self-Organizing Maps for imprecise data

被引：12
|
作者：
D'Urso, Pierpaolo [1 ]
De Giovanni, Livia [2 ]
Massari, Riccardo [1 ]
机构：
[1] Univ Roma La Sapienza, Dipartimento Sci Sociali & Econ, I-00185 Rome, Italy
[2] LUISS Guido Carli, Dipartimento Sci Polit, I-00197 Rome, Italy
来源：
FUZZY SETS AND SYSTEMS | 2014年 / 237卷
关键词：
Imprecise data; Fuzziness; Distance measures for imprecise data; SOMs for imprecise data; Vector quantization for imprecise data; FUZZY-SETS; CLUSTERING PROCEDURES; SIMILARITY MEASURES; DISTANCES; NUMBERS;
D O I：
10.1016/j.fss.2013.09.011
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Self-Organizing Maps (SOMs) consist of a set of neurons arranged in such a way that there are neighbourhood relationships among neurons. Following an unsupervised learning procedure, the input space is divided into regions with common nearest neuron (vector quantization), allowing clustering of the input vectors. In this paper, we propose an extension of the SOMs for data imprecisely observed (Self-Organizing Maps for imprecise data, SOMs-ID). The learning algorithm is based on two distances for imprecise data. In order to illustrate the main features and to compare the performances of the proposed method, we provide a simulation study and different substantive applications. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：63 / 89
页数：27
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