Title page for etd-0731102-205308


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URN etd-0731102-205308
Author Ching-Ting Yang
Author's Email Address m8942607@student.nsysu.edu.tw
Statistics This thesis had been viewed 5332 times. Download 3992 times.
Department Information Management
Year 2001
Semester 2
Degree Master
Type of Document
Language English
Title Time-Series Classification: Technique Development and Empirical Evaluation
Date of Defense 2002-07-23
Page Count 53
Keyword
  • Telecommunications Data Mining
  • Time-Series Similarity
  • Data Mining
  • k Nearest Neighbor Classification
  • Churn Prediction
  • Time-Series Classification
  • Abstract Many interesting applications involve decision prediction based on a time-series sequence or a set of time-series sequences, which are referred to as time-series classification problems. Past classification analysis research predominately focused on constructing a classification model from training instances whose attributes are atomic and independent. Direct application of traditional classification analysis techniques to time-series classification problems requires the transformation of time-series data into non-time-series data attributes by applying some statistical operations (e.g., average, sum, etc). However, such statistical transformation often results in information loss. In this thesis, we proposed the Time-Series Classification (TSC) technique, based on the nearest neighbor classification approach. The result of empirical evaluation showed that the proposed time-series classification technique had better performance than the statistical-transformation-based approach.
    Advisory Committee
  • Tung-Ching Lin - chair
  • Fu-Ren Lin - co-chair
  • Chih-Ping Wei - advisor
  • Files
  • etd-0731102-205308.pdf
  • indicate access worldwide
    Date of Submission 2002-07-31

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