Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering
This paper attempts to reduce the complexity of aesthetical conceptual system rule base by using fuzzy clustering method to make our decision making nearer to human computer interaction ideals by using the concept of similarity. Where we have a rule base of our aesthetical concept called Aesthetical...
Saved in:
Main Authors: | , , |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2007
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/3121/1/Reza-CITA-paper180-1.pdf http://eprints.utm.my/id/eprint/3121/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Teknologi Malaysia |
Language: | English |
id |
my.utm.3121 |
---|---|
record_format |
eprints |
spelling |
my.utm.31212017-08-07T01:27:07Z http://eprints.utm.my/id/eprint/3121/ Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering Mashinchi, M. Reza Mashinchi, M. Hadi Selamat, Ali QA76 Computer software This paper attempts to reduce the complexity of aesthetical conceptual system rule base by using fuzzy clustering method to make our decision making nearer to human computer interaction ideals by using the concept of similarity. Where we have a rule base of our aesthetical concept called Aesthetical Fuzzy Rule Base (AFRB) we have numbers of aesthetical concept rules. To make our AFRB more usable and preparing it for better interaction with human, it must be clustered, whereas we are dealing in our interaction with computer by uncertainties of aesthetical semantics. Finally, we will show an appropriate clustering called fuzzy clustering by reasoning for our AFRB. 2007 Conference or Workshop Item NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/3121/1/Reza-CITA-paper180-1.pdf Mashinchi, M. Reza and Mashinchi, M. Hadi and Selamat, Ali (2007) Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering. In: -. (Unpublished) |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
QA76 Computer software |
spellingShingle |
QA76 Computer software Mashinchi, M. Reza Mashinchi, M. Hadi Selamat, Ali Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering |
description |
This paper attempts to reduce the complexity of aesthetical conceptual system rule base by using fuzzy clustering method to make our decision making nearer to human computer interaction ideals by using the concept of similarity. Where we have a rule base of our aesthetical concept called Aesthetical Fuzzy Rule Base (AFRB) we have numbers of aesthetical concept rules. To make our AFRB more usable and preparing it for better interaction with human, it must be clustered, whereas we are dealing in our interaction with computer by uncertainties of aesthetical semantics. Finally, we will show an appropriate clustering called fuzzy clustering by reasoning for our AFRB. |
format |
Conference or Workshop Item |
author |
Mashinchi, M. Reza Mashinchi, M. Hadi Selamat, Ali |
author_facet |
Mashinchi, M. Reza Mashinchi, M. Hadi Selamat, Ali |
author_sort |
Mashinchi, M. Reza |
title |
Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering
|
title_short |
Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering
|
title_full |
Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering
|
title_fullStr |
Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering
|
title_full_unstemmed |
Aesthetical concept for human-computer interaction using fuzzy knowledge base clustering
|
title_sort |
aesthetical concept for human-computer interaction using fuzzy knowledge base clustering |
publishDate |
2007 |
url |
http://eprints.utm.my/id/eprint/3121/1/Reza-CITA-paper180-1.pdf http://eprints.utm.my/id/eprint/3121/ |
_version_ |
1643643740595683328 |