Similarity reasoning in formal concept analysis: from one- to many-valued contexts

被引：24
作者：
Formica, Anna [1 ]
机构：
[1] CNR, Ist Anal Sistemi & Informat IASI Antonio Ruberti, Via Taurini 19, I-00185 Rome, Italy
来源：
KNOWLEDGE AND INFORMATION SYSTEMS | 2019年 / 60卷 / 02期
关键词：
Formal concept analysis; Similarity reasoning; Interval type-2 fuzzy sets; Many-valued contexts; FCA with interordinal scaling; FUZZY-LOGIC SYSTEMS; SEMANTIC SIMILARITY; INFORMATION-CONTENT; SETS; WORDS; MODEL;
D O I：
10.1007/s10115-018-1252-4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, concept similarity in formal concept analysis (FCA) with many-valued contexts is addressed. In particular, this work focuses on FCA many-valued contexts where attribute values are intervals (FCA with interordinal scaling), here referred to as IFCA. IFCA is based on interval type-2 fuzzy sets, which provide a simplification of the more general type-2 fuzzy sets. In this work, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated in the literature, although the increasing interest in the combination of FCA with fuzzy sets. Note that the topic addressed in this paper is presented by providing simple examples in order to reach a broad audience of readers.
引用
收藏
页码：715 / 739
页数：25
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