By Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline Robardet, Arno Siebes, Jean-François Boulicaut (eds.)
This ebook constitutes the refereed lawsuits of the eighth foreign 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 displays, provided have been conscientiously reviewed and chosen from virtually eighty submissions. All present facets of this interdisciplinary box are addressed; for instance interactive instruments to steer and help information research in complicated situations, expanding availability of immediately accumulated facts, instruments that intelligently help and help human analysts, tips to regulate clustering effects and isotonic type timber. more often than not the parts coated comprise data, laptop studying, info mining, type and development acceptance, clustering, purposes, modeling, and interactive dynamic facts visualization.
Read Online or Download Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings PDF
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Additional info for Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings
We present a novel space partitioning scheme called the kdq-tree that we use to construct multi-dimensional histograms. Its size grows linearly with the dimensionality and the size of the data, at the same time generates cells with good aspect ratio. Both of these properties are important for accurate histograms. – We overcome the issue of data sparsity in very high dimensions by transforming the original data to a lower dimensional space using referential distances and applying the KL change detection to the transformed data.
This is principally because these tests rely on data being ordered (they are rank-based statistics), and thus in two or more dimensions, the lack of a unique ordering renders them ineﬀective. 2 Related Work The Kullback-Leibler Distance. The Kullback-Leibler distance is one of the most fundamental measures in information theory. It also has a natural Change (Detection) You Can Believe in: Finding Distributional Shifts 23 interpretation as a distance function between distributions, being a special case of an Ali-Silvey and Bregman distance.
2. (a) 2-D projection of a 100-D data stream which has level shifts and dispersion changes. (b) Time series plot of one of the variables. 10. The KL method generates numerous alarms (red tick marks) while the RD method (black tick marks) generates alarms only near the change points. Uniform distributions. The spatial representation is shown in Figure 1 (a) in blue, red and green. The temporal representation in Figure 1(b) shows that the data stream starts with two clusters (blue) of equal mass that start moving together at t=2000 and continue to get closer (red) until t=8000.
Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings by Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline Robardet, Arno Siebes, Jean-François Boulicaut (eds.)