scholarly journals eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

2021 ◽  
Vol 10 (4) ◽  
pp. 2212-2222
Author(s):  
Alvincent E. Danganan ◽  
Edjie Malonzo De Los Reyes
Keyword(s):  
Data Analysis ◽  
Clustering Algorithm ◽  
Median Absolute Deviation ◽  
Accuracy Rate ◽  
Absolute Deviation ◽  
Improvement Rate ◽  
Overlapping Clustering ◽  
Overlapping Clusters ◽  
Runtime Performance

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

scholarly journals Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 339 ◽  
Author(s):  
Yongsong Li ◽  
Zhengzhou Li ◽  
Kai Wei ◽  
Weiqi Xiong ◽  
Jiangpeng Yu ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Haar Wavelet ◽  
Image Sensor ◽  
Median Absolute Deviation ◽  
Noise Estimation ◽  
Estimation Methods ◽  
Absolute Deviation

Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

Alternatives to the Median Absolute Deviation

1993 ◽  
Vol 88 (424) ◽  
pp. 1273-1283 ◽  
Author(s):  
Peter J. Rousseeuw ◽  
Christophe Croux
Keyword(s):  
Median Absolute Deviation ◽  
Absolute Deviation

scholarly journals A Comparison Between New Modification of ANWK and Classical ANWK Methods in Nonparametric Regression

2021 ◽  
Vol 5 (2) ◽  
pp. 32-37
Author(s):  
Hazhar T. A. Blbas ◽  
Wasfi T. Kahwachi
Keyword(s):  
Standard Deviation ◽  
Kernel Estimator ◽  
Median Absolute Deviation ◽  
Interquartile Range ◽  
Kernel Estimators ◽  
Statistical Research ◽  
Absolute Deviation ◽  
Research Fields ◽  
Kernel Estimations ◽  
Nonparametric Kernel

Nonparametric kernel estimators are mostly used in a variety of statistical research fields. Nadaraya-Watson kernel estimator (NWK) is one of the most important nonparametric kernel estimator that is often used in regression models with a fixed bandwidth. In this article, we consider the four new Proposed Adaptive Nadaraya-Watson Kernel Regression Estimators (Interquartile Range, Standard Deviation, Mean Absolute Devotion, and Median Absolute Deviation) rather than (Fixed Bandwidth, Adaptive Geometric, Adaptive Mean, Adaptive Range, and Adaptive Median). The outcomes in both simulation and actual data in Leukemia Cancer show that the four new ANW Kernel Estimators (Interquartile Range, Standard Deviation, Mean Absolute devotion, and Median Absolute Deviation) is more effective than the kernel estimations with fixed bandwidth in previous studies using Mean Square Error (MSE) Criterion.

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