Analyzing Data Changes using Mean Shift Clustering

2016 ◽  
Vol 30 (07) ◽  
pp. 1650016 ◽  
Author(s):  
Nir Sharet ◽  
Ilan Shimshoni
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Mean Shift ◽  
Stereo Image ◽  
Learning Method ◽  
Training Set ◽  
Local Cluster ◽  
Mean Shift Clustering ◽  
Cluster Distribution ◽  
The Mean

A nonparametric unsupervised method for analyzing changes in complex datasets is proposed. It is based on the mean shift clustering algorithm. Mean shift is used to cluster the old and new datasets and compare the results in a nonparametric manner. Each point from the new dataset naturally belongs to a cluster of points from its dataset. The method is also able to find to which cluster the point belongs in the old dataset and use this information to report qualitative differences between that dataset and the new one. Changes in local cluster distribution are also reported. The report can then be used to try to understand the underlying reasons which caused the changes in the distributions. On the basis of this method, a transductive transfer learning method for automatically labeling data from the new dataset is also proposed. This labeled data is used, in addition to the old training set, to train a classifier better suited to the new dataset. The algorithm has been implemented and tested on simulated and real (a stereo image pair) datasets. Its performance was also compared with several state-of-the-art methods.

Video Vehicle Tracking Based on Improved Mean-Shift Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 1408-1411
Author(s):  
Wei Bin Chen ◽  
Xin Zhang ◽  
Su Qin Luo
Keyword(s):  
Clustering Algorithm ◽  
Mean Shift ◽  
Vehicle Tracking ◽  
Absolute Difference ◽  
Mean Shift Algorithm ◽  
Invariant Moments ◽  
Mean Shift Clustering ◽  
Video Frames ◽  
Detection Technology ◽  
Background Area

An improved Mean-Shift-based Video vehicle tracking algorithm was proposed and which can improve the real-time and accuracy of the vehicle detection technology in the application. First, it eliminates the disturbance from unrelated background by mathematical morphology operation between a traffic image and the mask of fixed background area .Then the image sequences are simulated by absolute difference of adaptive threshold for detecting latent target. At last, clusters video frames with similar characteristics which are regarded of the invariant moments vectors by Mean Shift clustering algorithm. Experimental results shown that the improved algorithm has advantages of reducing king region of vehicle matching and vehicle complete occlusion.

scholarly journals A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146352 ◽  
Author(s):  
Daniel M. de Brito ◽  
Vinicius Maracaja-Coutinho ◽  
Savio T. de Farias ◽  
Leonardo V. Batista ◽  
Thaís G. do Rêgo
Keyword(s):  
Clustering Algorithm ◽  
Mean Shift ◽  
Genomic Islands ◽  
Mean Shift Clustering ◽  
Novel Method

Log-Based Anomaly Detection with the Improved K-Nearest Neighbor

2020 ◽  
Vol 30 (02) ◽  
pp. 239-262 ◽  
Author(s):  
Bingming Wang ◽  
Shi Ying ◽  
Guoli Cheng ◽  
Rui Wang ◽  
Zhe Yang ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Keyword Search ◽  
Mean Shift ◽  
Recall Rate ◽  
K Nearest Neighbor ◽  
Mean Shift Clustering ◽  
Negative Effect

Logs play an important role in the maintenance of large-scale systems. The number of logs which indicate normal (normal logs) differs greatly from the number of logs that indicate anomalies (abnormal logs), and the two types of logs have certain differences. To automatically obtain faults by K-Nearest Neighbor (KNN) algorithm, an outlier detection method with high accuracy, is an effective way to detect anomalies from logs. However, logs have the characteristics of large scale and very uneven samples, which will affect the results of KNN algorithm on log-based anomaly detection. Thus, we propose an improved KNN algorithm-based method which uses the existing mean-shift clustering algorithm to efficiently select the training set from massive logs. Then we assign different weights to samples with different distances, which reduces the negative effect of unbalanced distribution of the log samples on the accuracy of KNN algorithm. By comparing experiments on log sets from five supercomputers, the results show that the method we proposed can be effectively applied to log-based anomaly detection, and the accuracy, recall rate and F measure with our method are higher than those of traditional keyword search method.

Ceramic Microstructure Image Segmentation by Mean Shift

2013 ◽  
Vol 423-426 ◽  
pp. 2602-2605 ◽  
Author(s):  
Hua Hui Cai ◽  
Yan Cheng ◽  
Bing Xiang Liu
Keyword(s):  
Image Processing ◽  
Median Filter ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Electronic Noise ◽  
Microscopic Image ◽  
Mean Shift Clustering ◽  
Ceramic Microstructure ◽  
The Mean ◽  
Microscopic Image Processing

In order to effectively assist the researchers conduct quantitative analysis of ceramic microstructures, a segmentation algorithm based on mean shift is used for the ceramic microstructure image. Since the collection and transfer process of microscopic image will inevitably be subject to uneven distribution of light, electronic noise and other interference factors which make the image quality deterioration, it is necessary to reduce noises and enhance edges for ceramic microscopic image processing at first. Therefore, the median filter is used to remove the noises in the ceramic microstructure images. Then the component with similar feature is separated and merged by the mean shift segmentation algorithm. Experiments show the proposed algorithm of using median filter and mean shift clustering gives satisfactory results.

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