{"title":"Classification of symbolic data using fuzzy set theory","authors":"M. Dinesh, K. Gowda, T. V. Ravi","doi":"10.1109/KES.1997.619413","DOIUrl":null,"url":null,"abstract":"Proposes a new algorithm to carry out classification of symbolic data using fuzzy set theory without any a priori assumption. The aim is to show how to apply fuzzy concepts to symbolic data. The new algorithm involves two stages. In the first stage, the number of classes present in the data is found using a cluster indicator, and in the second stage, fuzzy descriptions on symbolic data have been developed. The proposed work is new in the sense that no research work has previously been reported on the application of fuzzy concepts to symbolic data classification. The results of the proposed algorithm are compared with other symbolic clustering techniques.","PeriodicalId":166931,"journal":{"name":"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1st International Conference on Conventional and Knowledge Based Intelligent Electronic Systems. KES '97","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KES.1997.619413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Proposes a new algorithm to carry out classification of symbolic data using fuzzy set theory without any a priori assumption. The aim is to show how to apply fuzzy concepts to symbolic data. The new algorithm involves two stages. In the first stage, the number of classes present in the data is found using a cluster indicator, and in the second stage, fuzzy descriptions on symbolic data have been developed. The proposed work is new in the sense that no research work has previously been reported on the application of fuzzy concepts to symbolic data classification. The results of the proposed algorithm are compared with other symbolic clustering techniques.
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