A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method

Data mining is an emerging technology where researchers explore innovative ideas in different domains, particularly detecting anomalies. Instances in the dataset which considerably deviate from others by their common patterns are known as anomalies. The state of being ambiguous and not affording cer...

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Autores principales:	T. Sangeetha, Geetha Mary A
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Data Mining Entropy Fuzzy Proximity Mixed Data Rough sets Weighted Density Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/a824f40922d94837bf595332b7694a55
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id	oai:doaj.org-article:a824f40922d94837bf595332b7694a55
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spelling	oai:doaj.org-article:a824f40922d94837bf595332b7694a552021-11-26T04:40:48ZA fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method2666-222110.1016/j.socl.2021.100027https://doaj.org/article/a824f40922d94837bf595332b7694a552021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666222121000162https://doaj.org/toc/2666-2221Data mining is an emerging technology where researchers explore innovative ideas in different domains, particularly detecting anomalies. Instances in the dataset which considerably deviate from others by their common patterns are known as anomalies. The state of being ambiguous and not affording certainty of data exists in this world of nature. Rough Set Theory is a proven methodology which deals with ambiguity and uncertainty of data. Research works that have been done until this point were focused on numeric or categorical type, which fails when the attributes are mixed type. By using fuzzy proximity and ordering relations, the numerical data has been converted to categorical data. This article presented an idea for detecting outliers in mixed data where the weighted density values of attributes and objects are calculated. The proposed approach has been compared with existing outlier detection methods by taking the hiring dataset as an example and benchmarked with Harvard dataverse datasets to prove its efficiency and performance.T. SangeethaGeetha Mary AElsevierarticleData MiningEntropyFuzzy ProximityMixed DataRough setsWeighted DensityInformation technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENSoft Computing Letters, Vol 3, Iss , Pp 100027- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data Mining Entropy Fuzzy Proximity Mixed Data Rough sets Weighted Density Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Data Mining Entropy Fuzzy Proximity Mixed Data Rough sets Weighted Density Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 T. Sangeetha Geetha Mary A A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
description	Data mining is an emerging technology where researchers explore innovative ideas in different domains, particularly detecting anomalies. Instances in the dataset which considerably deviate from others by their common patterns are known as anomalies. The state of being ambiguous and not affording certainty of data exists in this world of nature. Rough Set Theory is a proven methodology which deals with ambiguity and uncertainty of data. Research works that have been done until this point were focused on numeric or categorical type, which fails when the attributes are mixed type. By using fuzzy proximity and ordering relations, the numerical data has been converted to categorical data. This article presented an idea for detecting outliers in mixed data where the weighted density values of attributes and objects are calculated. The proposed approach has been compared with existing outlier detection methods by taking the hiring dataset as an example and benchmarked with Harvard dataverse datasets to prove its efficiency and performance.
format	article
author	T. Sangeetha Geetha Mary A
author_facet	T. Sangeetha Geetha Mary A
author_sort	T. Sangeetha
title	A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
title_short	A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
title_full	A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
title_fullStr	A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
title_full_unstemmed	A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
title_sort	fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/a824f40922d94837bf595332b7694a55
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A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method

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