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      Diversity based Relevance Feedback for Time Series Search

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      Author
      Eravci, B.
      Ferhatosmanoglu H.
      Date
      2013
      Source Title
      Proceedings of the VLDB Endowment
      Print ISSN
      21508097
      Volume
      7
      Issue
      2
      Pages
      109 - 120
      Language
      English
      Type
      Article
      Item Usage Stats
      131
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      Abstract
      We propose a diversity based relevance feedback approach for time series data to improve the accuracy of search results. We first develop the concept of relevance feedback for time series based on dual-tree complex wavelet (CWT) and SAX based approaches. We aim to enhance the search quality by incorporating diversity in the results presented to the user for feedback. We then propose a method which utilizes the representation type as part of the feedback, as opposed to a human choosing based on a preprocessing or training phase. The proposed methods utilize a weighting to handle the relevance feedback of important properties for both single and multiple representation cases. Our experiments on a large variety of time series data sets show that the proposed diversity based relevance feedback improves the retrieval performance. Results confirm that representation feedback incorporates item diversity implicitly and achieves good performance even when using simple nearest neighbor as the retrieval method. To the best of our knowledge, this is the first study on diversification of time series search to improve retrieval accuracy and representation feedback. © 2013 VLDB Endowment.
      Keywords
      Dual-tree complex wavelets
      Multiple representation
      Nearest neighbors
      Relevance feedback
      Representation type
      Retrieval accuracy
      Retrieval performance
      Time series searches
      Information retrieval
      Time series
      Wavelet transforms
      Feedback
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      http://hdl.handle.net/11693/20798
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      • Department of Computer Engineering 1370
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