Information Cell Mixture Models: The Cognitive Representation of Vague Concepts

Tang Yongchuan, J Lawry

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

Abstract

Based on prototype theory for vague concept modelling, a transparent cognitive structure named information cell mixture model is proposed to represent the semantics of vague concept. An information cell mixture model on domain Ω is actually a set of weighted information cells L i s, where each information cell L i has a transparent cognitive structure ′L i  = about P i ′ which is mathematically formalized by a 3-tuple 〈P i ,d i ,δ i 〉 comprising a prototype set Pi(⊆Ω), a distance function d i on Ω and a density function δ i on [0,+∞). A positive neighborhood function of the information cell mixture model is introduced in this paper to reflect the belief distribution of positive neighbors of the underlying concept. An information cellularization algorithm is also proposed to learn the information cell mixture model from training data set, which is a direct application of k-means and EM algorithms. This novel transparent cognitive structure of vague concept provides a powerful tool for information coarsening and concept modelling, and has potential application in uncertain reasoning and classification.
Translated title of the contributionInformation Cell Mixture Models: The Cognitive Representation of Vague Concepts
Original languageEnglish
Title of host publicationIntegrated Uncertainty Management and Applications
PublisherSpringer Berlin Heidelberg
Pages371 - 381
Number of pages10
DOIs
Publication statusPublished - Apr 2010

Publication series

NameAdvances in Intelligent and Soft Computing
Volume68

Bibliographical note

Editors: Van-Nam Hutnh etal
Name and Venue of Conference: Integrated Uncertainty Management, JAIST, Japan

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