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CCLS Internship Presentation

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Charles Naut
Charles Naut

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

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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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CCLS Internship Presentation

  • 1. Data Mining and Feeder Attribute Analysis Interns Charles Naut Lawrence Ng Columbia University’s Center for Computational Learning Systems
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  • 8. Dendrogram of Distances Between Feeders Based on Feeder Attributes
  • 9. Pairing of Feeders List of feeders and companions based on increasing coverage area.
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