K-medoids method based on divergence for uncertain data clustering

2016 ◽  
Author(s):  
Jin Zhou ◽  
Yuqi Pan ◽  
C. L. Philip Chen ◽  
Dong Wang ◽  
Shiyuan Han
Keyword(s):  
Data Clustering ◽  
Uncertain Data

On Fuzzy Non-Metric Model for Data with Tolerance and its Application to Incomplete Data Clustering

2016 ◽  
Vol 20 (4) ◽  
pp. 571-579 ◽  
Author(s):  
Yasunori Endo ◽  
◽  
Tomoyuki Suzuki ◽  
Naohiko Kinoshita ◽  
Yukihiro Hamasuna ◽  
...  
Keyword(s):  
Data Clustering ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Uncertain Data ◽  
Data Sets ◽  
Membership Degree ◽  
Clustering Methods ◽  
Clustering Method ◽  
Numerical Examples ◽  
Metric Model

The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some clustering methods were constructed according to this concept, e.g. fuzzyc-means for data with tolerance. These methods can handle uncertain data in the framework of optimization. Thus, in the present study, we apply the concept to FNM. First, we propose a new clustering algorithm based on FNM using the concept of tolerance, which we refer to as the fuzzy non-metric model for data with tolerance. Second, we show that the proposed algorithm can handle incomplete data sets. Third, we verify the effectiveness of the proposed algorithm based on comparisons with conventional methods for incomplete data sets in some numerical examples.

Uncertain Data Clustering in Distributed Peer-to-Peer Networks

2018 ◽  
Vol 29 (6) ◽  
pp. 2392-2406 ◽  
Author(s):  
Jin Zhou ◽  
Long Chen ◽  
C. L. Philip Chen ◽  
Yingxu Wang ◽  
Han-Xiong Li
Keyword(s):  
Data Clustering ◽  
Uncertain Data ◽  
Peer To Peer ◽  
Peer Networks ◽  
Peer To Peer Networks

scholarly journals A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 875
Author(s):  
Chao Li ◽  
Zhenjiang Zhang ◽  
Wei Wei ◽  
Han-Chieh Chao ◽  
Xuejun Liu
Keyword(s):  
Data Clustering ◽  
Possible Worlds ◽  
Probability Distributions ◽  
Uncertain Data ◽  
Estimation Algorithm ◽  
Measured Data ◽  
Possible World ◽  
Estimation Model ◽  
Leibler Divergence ◽  
Multiple Measurements

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.

Uncertain Data Clustering Based on Probability Distribution in Obstacle Space

2020 ◽  
Vol 111 (4) ◽  
pp. 2191-2214
Author(s):  
Jing Wan ◽  
Meiyu Cui ◽  
Yunbin He ◽  
Song Li
Keyword(s):  
Probability Distribution ◽  
Data Clustering ◽  
Uncertain Data

scholarly journals Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks

Sensors ◽  
2014 ◽  
Vol 14 (4) ◽  
pp. 6584-6605 ◽  
Author(s):  
Qinghua Luo ◽  
Yu Peng ◽  
Xiyuan Peng ◽  
Abdulmotaleb Saddik
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Clustering ◽  
Uncertain Data ◽  
Distance Estimation ◽  
Wireless Sensor
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