A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition
In the field of pattern recognition, clustering is used to group the data into different clusters based on the similarity among them. There are a number of clustering techniques developed in the past using different distance/similarity measure. Due to the high versatility in data, researchers have u...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2017-01-01
|
Series: | Cogent Mathematics |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/23311835.2017.1385374 |
id |
doaj-425ddca138594c728aabd0937223ce3d |
---|---|
record_format |
Article |
spelling |
doaj-425ddca138594c728aabd0937223ce3d2020-11-25T01:30:55ZengTaylor & Francis GroupCogent Mathematics2331-18352017-01-014110.1080/23311835.2017.13853741385374A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognitionMohd Shoaib Khan0Q. M. Danish Lohani1M. Mursaleen2South Asian UniversitySouth Asian UniversityAligarh Muslim UniversityIn the field of pattern recognition, clustering is used to group the data into different clusters based on the similarity among them. There are a number of clustering techniques developed in the past using different distance/similarity measure. Due to the high versatility in data, researchers have used various distance measure like Hamming distance, Euclidean distance etc. to solve the clustering problems. In this paper, we proposed a novel similarity measure based on the double sequence space and modulus function. Also, to handle the uncertainty of data, Atanassov intuitionistic fuzzy set were used. Experimental simulation is performed on the real-world problems viz. car data and medical diagnosis problems and shows that the results are outperformed.http://dx.doi.org/10.1080/23311835.2017.1385374Atanassov intuitionistic fuzzy setclusteringdouble sequencemodulus functionsimilarity measure |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mohd Shoaib Khan Q. M. Danish Lohani M. Mursaleen |
spellingShingle |
Mohd Shoaib Khan Q. M. Danish Lohani M. Mursaleen A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition Cogent Mathematics Atanassov intuitionistic fuzzy set clustering double sequence modulus function similarity measure |
author_facet |
Mohd Shoaib Khan Q. M. Danish Lohani M. Mursaleen |
author_sort |
Mohd Shoaib Khan |
title |
A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition |
title_short |
A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition |
title_full |
A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition |
title_fullStr |
A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition |
title_full_unstemmed |
A novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition |
title_sort |
novel intuitionistic fuzzy similarity measure based on double sequence by using modulus function with application in pattern recognition |
publisher |
Taylor & Francis Group |
series |
Cogent Mathematics |
issn |
2331-1835 |
publishDate |
2017-01-01 |
description |
In the field of pattern recognition, clustering is used to group the data into different clusters based on the similarity among them. There are a number of clustering techniques developed in the past using different distance/similarity measure. Due to the high versatility in data, researchers have used various distance measure like Hamming distance, Euclidean distance etc. to solve the clustering problems. In this paper, we proposed a novel similarity measure based on the double sequence space and modulus function. Also, to handle the uncertainty of data, Atanassov intuitionistic fuzzy set were used. Experimental simulation is performed on the real-world problems viz. car data and medical diagnosis problems and shows that the results are outperformed. |
topic |
Atanassov intuitionistic fuzzy set clustering double sequence modulus function similarity measure |
url |
http://dx.doi.org/10.1080/23311835.2017.1385374 |
work_keys_str_mv |
AT mohdshoaibkhan anovelintuitionisticfuzzysimilaritymeasurebasedondoublesequencebyusingmodulusfunctionwithapplicationinpatternrecognition AT qmdanishlohani anovelintuitionisticfuzzysimilaritymeasurebasedondoublesequencebyusingmodulusfunctionwithapplicationinpatternrecognition AT mmursaleen anovelintuitionisticfuzzysimilaritymeasurebasedondoublesequencebyusingmodulusfunctionwithapplicationinpatternrecognition AT mohdshoaibkhan novelintuitionisticfuzzysimilaritymeasurebasedondoublesequencebyusingmodulusfunctionwithapplicationinpatternrecognition AT qmdanishlohani novelintuitionisticfuzzysimilaritymeasurebasedondoublesequencebyusingmodulusfunctionwithapplicationinpatternrecognition AT mmursaleen novelintuitionisticfuzzysimilaritymeasurebasedondoublesequencebyusingmodulusfunctionwithapplicationinpatternrecognition |
_version_ |
1725088942388150272 |