{"title":"Multiple source clustering: a probabilistic reasoning approach","authors":"T. J. Leih, J. Harmse, E. Giannopoulos","doi":"10.1109/ADFS.1996.581097","DOIUrl":null,"url":null,"abstract":"In this paper we describe a versatile multiple source clustering (MSC) algorithm. The algorithm uses a form of probabilistic reasoning known as Bayesian networks to solve the MSC problem of incomparable feature spaces. For time-tagged data, the algorithm uses fuzzy conjunctions to support cluster formation and management. Clustering performance measures are defined and a multiple target tracking/multiple sensor example is presented.","PeriodicalId":254509,"journal":{"name":"Proceeding of 1st Australian Data Fusion Symposium","volume":"139 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeding of 1st Australian Data Fusion Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ADFS.1996.581097","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper we describe a versatile multiple source clustering (MSC) algorithm. The algorithm uses a form of probabilistic reasoning known as Bayesian networks to solve the MSC problem of incomparable feature spaces. For time-tagged data, the algorithm uses fuzzy conjunctions to support cluster formation and management. Clustering performance measures are defined and a multiple target tracking/multiple sensor example is presented.
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