Analyzing Data Changes using Mean Shift Clustering

Nir Sharet, Ilan Shimshoni

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number1650016
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number7
StatePublished - 1 Aug 2016

Bibliographical note

Publisher Copyright:
© 2016 World Scientific Publishing Company.


  • Change detection
  • cluster analysis
  • mean shift clustering

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


Dive into the research topics of 'Analyzing Data Changes using Mean Shift Clustering'. Together they form a unique fingerprint.

Cite this