CCLS Internship Presentation

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A presentation about the work me and my fellow intern did during our summer at Columbia University Center for Computational Learning Systems.

Tecnologia

Data Mining and Feeder Attribute Analysis Interns Charles Naut Lawrence Ng Columbia University’s Center for Computational Learning Systems

Transformer Attribute Time Series ,[object Object],[object Object],[object Object],41.9 61 40.9 60 … … … … 18.9 1 19.2 128 19.3 127 18.6 2 Load Hour

Piecewise Aggregate Approximation (PAA ) Symbolic Aggregate Approximation (SAX) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],1- E. Keogh, J. Lin & A. Fu (2005). HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 226 - 233., Houston, Texas, Nov 27-30, 2005. 2- B. Yi, & C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In Proc. of the 26th Int'l Conf. on Very Large Databases. pp 385-394, 2000.

SAX Conversion Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

SAX Goals ,[object Object],[object Object],[object Object],[object Object],[object Object]

Tarzan and Hot SAX ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],3- S.Lonardi, J. Lin, E. Keogh & B. Chiu (2007). Efficient Discovery of Unusual Patterns in Time Series. Special Issue of New Generation Computing Journal. To Appear. 4- E. Keogh, J. Lin and A. Fu (2005). HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 226 - 233., Houston, Texas, Nov 27-30, 2005.

Feeder Attribute Analysis ,[object Object],[object Object],[object Object]

Dendrogram of Distances Between Feeders Based on Feeder Attributes

Pairing of Feeders List of feeders and companions based on increasing coverage area.

Accomplishments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Growth ,[object Object],[object Object],[object Object],[object Object]

Special Thanks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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