A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs
Abstract
1. Introduction
2. Related Works
3. Preliminaries
3.1. Definition of Possible World
3.2. Definition of Kullback–Leibler Divergence
3.3. Some Assumptions
4. Possible World-Based Fusion Estimation Model (PWFWM)
4.1. Data Fusion Estimation
4.2. Distance Calculation Method Based on KL Divergence-Based Distance
4.3. The Clustering Method Based on the Possible World
Algorithm 1 for Matrix Pruning: |
Input: the matrix S ∈ Rn×n and pruning threshold p The processing: Removing step: For i = 1 to n For j = 1 to n If sij < p sij = 0 End if End for End for Normalization step: For i = 1 to n For j = 1 to n End for End for |
Algorithm 2 for processing S: |
Input: the matrix S ∈ Rn×n and clustering threshold Th The processing: is the set of eigenvalues of Ls is the set of eigenvectors of Ls If k = r End if |
4.4. Updating
Algorithm 3 for Clustering Updating: |
Input: the center of each cluster , and the number of cluster members of training set and the test set . The processing: Clustering step: = . For i = n + 1 to n + p If clusteri = clusteri’ Oi belongs to clusteri. Else if Oi belongs to clusteri’ Else Oi belongs to clusteri End if End if End for Centers updating step: For i = n + 1 to n + p If Oi belongs to clusteri End if End for |
5. Simulations
Algorithm 4 The Generation Method from Numerical Data to Uncertain Data (Gaussian Type). |
Input: the numerical data and the standard deviation of each attribute Output: the corresponding uncertain data For i = 1 to n x = random, 0 < x ≤ 1 End for |
5.1. The Clustering Accuracy
5.2. The Simulation with a Specific Dataset
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dataset | Objects | Attributes | Classes |
---|---|---|---|
Iris | 150 | 4 | 3 |
Wine | 178 | 13 | 3 |
Glass | 214 | 9 | 6 |
Ecoli | 327 | 7 | 5 |
Waveform | 5000 | 21 | 3 |
PhishingData [26] | 1353 | 9 | 3 |
UK-Means | REP | PWCLU | PWFEM-nd | PWFEM-pd | ||
---|---|---|---|---|---|---|
Iris | Max | 0.8800 | 0.8133 | 0.8133 | 0.8133 | 0.8533 |
Min | 0.5533 | 0.5533 | 0.5400 | 0.4867 | 0.5200 | |
Mean | 0.7244 | 0.6994 | 0.6869 | 0.7181 | 0.7602 | |
Variance | 0.0022 | 0.0021 | 0.0016 | 0.0017 | 0.0028 | |
Wine | Max | 0.7022 | 0.7022 | 0.5730 | 0.7079 | 0.9607 |
Min | 0.7022 | 0.7022 | 0.5730 | 0.6966 | 0.3202 | |
Mean | 0.7022 | 0.7022 | 0.5730 | 0.6989 | 0.8999 | |
Variance | 0 | 0 | 0 | 0 | 0.0173 | |
Glass | Max | 0.8333 | 0.7619 | 0.8618 | 0.7905 | 0.9286 |
Min | 0.2476 | 0.6000 | 0.2571 | 0.2286 | 0.3095 | |
Mean | 0.7239 | 0.7078 | 0.7588 | 0.6489 | 0.7818 | |
Variance | 0.0191 | 0.0010 | 0.0204 | 0.0173 | 0.0537 | |
Ecoli | Max | 0.5327 | 0.4953 | 0.5374 | 0.5234 | 0.5421 |
Min | 0.3458 | 0.2056 | 0.4065 | 0.3318 | 0.4299 | |
Mean | 0.4422 | 0.4025 | 0.4905 | 0.4527 | 0.4634 | |
Variance | 0.0012 | 0.0035 | 0.0011 | 0.0014 | 0.0009 | |
Waveform | Max | 0.5291 | 0.4006 | 0.7003 | 0.5199 | 0.7156 |
Min | 0.3180 | 0.2324 | 0.3945 | 0.4006 | 0.4801 | |
Mean | 0.4350 | 0.3177 | 0.5403 | 0.4445 | 0.5706 | |
Variance | 0.0014 | 0.0013 | 0.0025 | 0.0006 | 0.0038 | |
PhishingData | Max | 0.5639 | 0.4560 | 0.5647 | 0.5188 | 0.6061 |
Min | 0.4664 | 0.3585 | 0.4568 | 0.4508 | 0.4797 | |
Mean | 0.5183 | 0.4218 | 0.5027 | 0.4910 | 0.5719 | |
Variance | 0.0005 | 0.0004 | 0.0004 | 0.0002 | 0.0010 |
UK-Means | REP | PWCLU | PWFEM-nd | PWFEM-pd | ||
---|---|---|---|---|---|---|
Iris | Max | 0.7854 | 0.6809 | 0.6716 | 0.6700 | 0.7396 |
Min | 0.2694 | 0.3898 | 0.2871 | 0.2213 | 0.3162 | |
Mean | 0.5374 | 0.5245 | 0.4834 | 0.5295 | 0.5927 | |
Variance | 0.0050 | 0.0027 | 0.0031 | 0.0033 | 0.0054 | |
Wine | Max | 0.4946 | 0.4946 | 0.3184 | 0.5389 | 0.9551 |
Min | 0.4946 | 0.4946 | 0.3184 | 0.5136 | 0.3146 | |
Mean | 0.4946 | 0.4946 | 0.3184 | 0.5209 | 0.8803 | |
Variance | 0 | 0 | 0 | 0 | 0.0198 | |
Glass | Max | 0.7001 | 0.6171 | 0.7522 | 0.6288 | 0.8671 |
Min | 0.0997 | 0.4028 | 0.1643 | 0.0320 | 0.2233 | |
Mean | 0.5511 | 0.5250 | 0.6094 | 0.4258 | 0.6911 | |
Variance | 0.0196 | 0.0019 | 0.0223 | 0.0204 | 0.0672 | |
Ecoli | Max | 0.6544 | 0.6544 | 0.7064 | 0.7125 | 0.7309 |
Min | 0.3731 | 0.3731 | 0.5199 | 0.4679 | 0.3639 | |
Mean | 0.4988 | 0.4988 | 0.6354 | 0.5629 | 0.5569 | |
Variance | 0.0050 | 0.0050 | 0.0034 | 0.0054 | 0.0079 | |
Waveform | Max | 0.3247 | 0.2548 | 0.4645 | 0.3104 | 0.4895 |
Min | 0.1195 | 0.1286 | 0.1282 | 0.1919 | 0.2545 | |
Mean | 0.2112 | 0.1909 | 0.3244 | 0.2392 | 0.3558 | |
Variance | 0.0017 | 0.0008 | 0.0022 | 0.0005 | 0.0025 | |
PhishingData | Max | 0.2517 | 0.1636 | 0.2416 | 0.2200 | 0.3190 |
Min | 0.1559 | 0.0594 | 0.1452 | 0.1313 | 0.1804 | |
Mean | 0.2088 | 0.1050 | 0.1880 | 0.1760 | 0.2803 | |
Variance | 0.0004 | 0.0004 | 0.0005 | 0.0003 | 0.0008 |
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Li, C.; Zhang, Z.; Wei, W.; Chao, H.-C.; Liu, X. A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs. Sensors 2021, 21, 875. https://doi.org/10.3390/s21030875
Li C, Zhang Z, Wei W, Chao H-C, Liu X. A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs. Sensors. 2021; 21(3):875. https://doi.org/10.3390/s21030875
Chicago/Turabian StyleLi, Chao, Zhenjiang Zhang, Wei Wei, Han-Chieh Chao, and Xuejun Liu. 2021. "A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs" Sensors 21, no. 3: 875. https://doi.org/10.3390/s21030875
APA StyleLi, C., Zhang, Z., Wei, W., Chao, H.-C., & Liu, X. (2021). A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs. Sensors, 21(3), 875. https://doi.org/10.3390/s21030875