Detecting changes in unlabeled data streams using martingale

Shen Shyang Ho, Harry Wechsler

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


The martingale framework for detecting changes in data stream, currently only applicable to labeled data, is extended here to unlabeled data using clustering concept. The one-pass incremental changedetection algorithm (i) does not require a sliding window on the data stream, (ii) does not require monitoring the performance of the clustering algorithm as data points are streaming, and (iii) works well for high-dimensional data streams. To enhance the performance of the martingale change detection method, the multiple martingale test method using multiple views is proposed. Experimental results show (i) the feasibility of the martingale method for detecting changes in unlabeled data streams, and (ii) the multiple-martingale test method compares favorably with alternative methods using the recall and precision measures for the video-shot change detection problem.

Original languageEnglish (US)
Pages (from-to)1912-1917
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - Dec 1 2007
Event20th International Joint Conference on Artificial Intelligence, IJCAI 2007 - Hyderabad, India
Duration: Jan 6 2007Jan 12 2007

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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