Multiple source clustering: A probabilistic reasoning approach

被引：2
|
作者：
Leih, TJ
Harmse, J
Giannopoulos, E
机构：
来源：
ADFS-96 - FIRST AUSTRALIAN DATA FUSION SYMPOSIUM | 1996年
关键词：
D O I：
10.1109/ADFS.1996.581097
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：141 / 146
页数：6
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