By Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline Robardet, Arno Siebes, Jean-François Boulicaut (eds.)
This publication constitutes the refereed court cases of the eighth foreign convention on clever info 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, awarded have been rigorously reviewed and chosen from virtually eighty submissions. All present elements of this interdisciplinary box are addressed; for instance interactive instruments to steer and aid information research in advanced eventualities, expanding availability of immediately accrued info, instruments that intelligently aid and support human analysts, easy methods to keep an eye on clustering effects and isotonic category timber. generally the components lined contain facts, computer studying, facts mining, category and trend reputation, clustering, functions, modeling, and interactive dynamic information visualization.
Read 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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Extra 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
In contrast to statistical methods, ML algorithms generate a model from data that contain missing values, and then use the model to perform classiﬁcation that imputes the missing values. These methods do not concentrate solely on identifying a replacement for a missing value, but on using available information to preserve relationships in the entire dataset. These studies have empirically been shown to perform better than ad-hoc methods. However they are not meant to detect the missing mechanism nor to use such mechanisms to improve accuracy of prediction.
The mass shifts from one cluster to another at the change point t=5000, as shown in Figure 3. Both KL and RD are able to detect the mass shift, shown in Figure 3(b). 1 Real Life Applications We applied our algorithm based on the KL and RD methods to two real life applications, each of which generates a multi-dimensional data stream. The applications are of critical importance to a large telecommunications corporation. File Descriptor Streams. The calls made on a telecommunications network are logged and written to ﬁles in a highly specialized format.
6 Conclusion and Discussion We have presented an eﬃcient, nonparametric, fully streaming algorithm for detecting distributional changes in multi-dimensional data streams within a statistically rigorous hypothesis testing framework. We describe a novel data structure, the kdq-tree, to maintain general purpose multi-dimensional histograms that oﬀer a granular summarization of the data stream. We draw upon the Kullback-Leibler distance to measure the distance between data stream distributions, either using the original data or its referential distance.