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Graduate Projects - Details

Computer Science Program

Project ID: 319
Author: Sreekrishna Nallela
Project Title: Probabilistic Discovery of Motifs in Primary Water Levels
Semester: Fall 2008
Committe Chair: Dr. Long-zhuang Li
Committee Member 1: Dr. John Fernandez
Committee Member 2: Dr. Mario Garcia
Project Description: The developed system is able to produce motifs by making use of data mining methods. Data mining is a method of gathering all useful information from large databases. Data mining methods greatly support efforts to predict future outcomes. The developed system identifies motifs in a given time series dataset and these patterns are employed to improve the prediction of future outcomes. The method used in the developed system is a data mining method called Symbolic Aggregate approXimation (SAX). Random Projection algorithm is used to discover the unknown time series motifs. These motifs are tested for accuracy by comparing them with the brute force algorithm. This method requires only one parameter to identify the time series discords, unlike other methods that require a large number of parameters.
Project URL:   319.pdf
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