Advances in Intelligent Data Analysis VIII: 8th by Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline

By Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline Robardet, Arno Siebes, Jean-François Boulicaut (eds.)

This booklet constitutes the refereed complaints of the eighth overseas convention on clever information research, IDA 2009, held in Lyon, France, August 31 – September 2, 2009.

The 33 revised papers, 18 complete oral shows and 15 poster and brief oral shows, offered have been rigorously reviewed and chosen from nearly eighty submissions. All present elements of this interdisciplinary box are addressed; for instance interactive instruments to lead and aid information research in advanced situations, expanding availability of instantly gathered info, instruments that intelligently help and help human analysts, the best way to regulate clustering effects and isotonic category bushes. as a rule the parts coated contain records, desktop studying, facts mining, type and trend reputation, clustering, functions, modeling, and interactive dynamic info visualization.

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Additional resources for Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings

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183–190 (1993) 18. : Inducing features of random fields. IEEE Trans. Pattern Analysis and Machine Intelligence 19, 380–393 (1995) 19. : Bootstrap estimate of Kullback-Liebler information for model selection. Statistica Sinica 7, 375–394 (1997) 20. : Statistical change detection for multidimensional data. In: ACM SIGKDD 2007, pp. 667–676 (2007) 21. : Online outlier detection in sensor data using non-parametric models. In: VLDB 2006, pp. 187–198 (2006) 22. : Random sampling with a reservoir. fr Abstract.

We provide a strong statistical basis for deciding distributional shifts in the data stream using confidence intervals for sample proportions. , time series data with seasonality and trends, and other types of distributional shifts. 3 Basic Overview of Our Approach Let x1 , x2 , . . be a stream of points in Rd . A window Wi,n denotes the sequence of points ending at xi of size n: Wi,n = (xi−n+1 , . . , xi ), i ≥ n. We will Change (Detection) You Can Believe in: Finding Distributional Shifts 25 drop the subscript n when the context is clear.

1. We conducted exhaustive experiments to investigate the behavior of the KL distance and the change detection algorithm. Our experiments indicated that the KL distance detects distributional changes effectively for different ranges of values of the important parameters of the underlying distributions such as the mean, the standard deviation and correlation coefficient. In order to test the efficacy of the change detection method, we constructed experiments where we varied the window size, the significance level, the bootstrap sample size, as well as the dimension and correlation structure between individual components.

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