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Displaying publications 1 to 7 of 7 publications associated with the Machine Learning Group in 2009:

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- Dan Preston, Pavlos Protopapas, Carla Brodley, Discovering arbitrary event types in time series,
*Statistical Analysis and Data Mining (Best of SDM'09)*, Volume 2 Issue 5-6, Pages 396 - 411, 2009 [+]

**Authors:**Dan Preston, Pavlos Protopapas, Carla BrodleyStatistical Analysis and Data Mining (Best of SDM'09)

Volume 2 Issue 5-6, Pages 396 - 411**Year:**2009**Abstract:**The discovery of events in time series can have important implications, such as identifying microlensing events in astronomical surveys, or changes in a patient's electrocardiogram. Current methods for identifying events require a sliding window of a fixed size, which is not ideal for all applications and could overlook important events. In this work, we develop probability models for calculating the significance of an arbitrary-sized sliding window and use these probabilities to find areas of significance. Because a brute force search of all sliding windows and all window sizes would be computationally intractable, we introduce a method for quickly approximating the results. We apply our method to over 100 000 astronomical time series from the MACHO survey, in which 56 different sections of the sky are considered, each with one or more known events. Our method was able to recover 100% of these events in the top 1% of the results, essentially pruning 99% of the data. Interestingly, our method was able to identify events that do not pass traditional event discovery procedures. In this extended work, we present a generalization of our algorithm to discover different event types characterized by distinct patterns.**Url:**http://www3.interscience.wiley.com/journal/123188740/abstract?CRETRY=1&SRETRY=0**Associated Research Topics:****Affiliated Tufts Members:**- None.

**Tufts / Purdue Alumni:** - Dan Preston, Carla Brodley, Pavlos Protopapas, Event Discovery in Time Series,
*Proceedings of the Ninth SIAM International Conference on Data Mining*, 2009 [+]

**Authors:**Dan Preston, Carla Brodley, Pavlos ProtopapasProceedings of the Ninth SIAM International Conference on Data Mining

**Year:**2009**Abstract:**The discovery of events in time series can have important implications, such as identifying microlensing events in astronomical surveys, or changes in a patient's electrocardiogram. Current methods for identifying events require a sliding window of a fixed size, which is not ideal for all applications and could overlook important events. In this work, we develop probability models for calculating the significance of an arbitrary-sized sliding window and use these probabilities to find areas of significance. Because a brute force search of all sliding windows and all window sizes would be computationally intractable, we introduce a method for quickly approximating the results. We apply our method to over 100,000 astronomical time series from the MACHO survey, in which 56 different sections of the sky are considered, each with one or more known events. Our method was able to recover 100% of these events in the top 1% of the results, essentially pruning 99% of the data. Interestingly, our method was able to identify events that do not pass traditional event discovery procedures.**Url:**http://www.siam.org/proceedings/datamining/2009/dm09_007_prestond.pdf**Associated Research Topics:****Affiliated Tufts Members:**- None.

**Tufts / Purdue Alumni:** - U. Rebbapragada, P. Protopapas, C. E. Brodley, C. Alcock, Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars,
*Machine Learning*, Vol. 74, Issue 3, p. 281, 2009 [+]

**Authors:**U. Rebbapragada, P. Protopapas, C. E. Brodley, C. AlcockMachine Learning

Vol. 74, Issue 3, p. 281**Year:**2009**Abstract:**Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection methods for unphased data, and show that PCAD’s reported anomalies are comparable to or better than all other methods. Finally, astrophysicists on our team have verified that PCAD finds true anomalies that might be indicative of novel astrophysical phenomena.**Url:**http://www.springerlink.com/content/l8r3689876024121/**Associated Research Topics:****Affiliated Tufts Members:**- None.

**Tufts / Purdue Alumni:** - Saket Joshi, Kristian Kersting, and Roni Khardon, Generalized First Order Decision Diagrams for First Order Markov Decision Processes,
*In the Proceedings of the International Joint Conference on Artificial Intelligence*, 2009 [+]

**Authors:**Saket Joshi, Kristian Kersting, and Roni KhardonIn the Proceedings of the International Joint Conference on Artificial Intelligence

**Year:**2009**Url:**http://www.cs.tufts.edu/~roni/PUB/ijcai09-GFODD.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - U. Rebbapragada, L. Mandrake, K. Wagstaff, D. Gleeson, R. Castaño, S. Chien, and C. E. Brodley, Improving Onboard Analysis of Hyperion Images by Filtering Mislabeled Training Data Examples,
*IEEE Aerospace Conference*, 2009 [+]

**Authors:**U. Rebbapragada, L. Mandrake, K. Wagstaff, D. Gleeson, R. Castaño, S. Chien, and C. E. BrodleyIEEE Aerospace Conference

**Year:**2009**Associated Research Topics:****Affiliated Tufts Members:**- None.

**Tufts / Purdue Alumni:** - G. Wachman, R. Khardon, P. Protopapas, and C. Alcock, Kernels for Periodic Time Series Arising in Astronomy,
*Proceedings of the European Conference on Machine Learning (ECML)*, 2009 [+]

**Authors:**G. Wachman, R. Khardon, P. Protopapas, and C. AlcockProceedings of the European Conference on Machine Learning (ECML)

**Year:**2009**Url:**http://www.cs.tufts.edu/~roni/PUB/ecml09-tskernels.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - L. Mandrake, K. Wagstaff, D. Gleeson, U. Rebbapragada, D. Tran, R. Castaño, S. Chien, R. Pappalardo, Onboard Detection of Naturally Occurring Sulfur Compounds on the Surface of a Glacier using an SVM and the Hyperion Multi-Spectral Instrument,
*IEEE Aerospace Conference*, 2009 [+]

**Authors:**L. Mandrake, K. Wagstaff, D. Gleeson, U. Rebbapragada, D. Tran, R. Castaño, S. Chien, R. PappalardoIEEE Aerospace Conference

**Year:**2009**Associated Research Topics:****Affiliated Tufts Members:**- None.

**Tufts / Purdue Alumni:**

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