Li Chao, Zhang Zhenjiang, Wei Wei, Chao Han-Chieh, Liu Xuejun
Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, The School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
The School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2021 Jan 28;21(3):875. doi: 10.3390/s21030875.
In data clustering, the measured data are usually regarded as uncertain data. As a probability-based clustering technique, possible world can easily cluster the uncertain data. However, the method of possible world needs to satisfy two conditions: determine the data of different possible worlds and determine the corresponding probability of occurrence. The existing methods mostly make multiple measurements and treat each measurement as deterministic data of a possible world. In this paper, a possible world-based fusion estimation model is proposed, which changes the deterministic data into probability distribution according to the estimation algorithm, and the corresponding probability can be confirmed naturally. Further, in the clustering stage, the Kullback-Leibler divergence is introduced to describe the relationships of probability distributions among different possible worlds. Then, an application in wearable body networks (WBNs) is given, and some interesting conclusions are shown. Finally, simulations show better performance when the relationships between features in measured data are more complex.
在数据聚类中，测量数据通常被视为不确定数据。作为一种基于概率的聚类技术，可能世界可以轻松地对不确定数据进行聚类。然而，可能世界的方法需要满足两个条件：确定不同可能世界的数据以及确定相应的发生概率。现有方法大多进行多次测量，并将每次测量视为一个可能世界的确定性数据。本文提出了一种基于可能世界的融合估计模型，该模型根据估计算法将确定性数据转换为概率分布，并且相应的概率可以自然地确定。此外，在聚类阶段，引入库尔贝克-莱布勒散度来描述不同可能世界之间概率分布的关系。然后，给出了在可穿戴人体网络（WBNs）中的应用，并展示了一些有趣的结论。最后，仿真表明，当测量数据中的特征之间的关系更复杂时，性能会更好。
