NDLI: Cluster-Based Similarity Search in Time Series

Content Provider	IEEE Xplore Digital Library
Author	Karamitopoulos, L. Evangelidis, G.
Copyright Year	2009
Abstract	In this paper, we present a new method that accelerates similarity search implemented via one-nearest neighbor on time series data. The main idea is to identify the most similar time series to a given query without necessarily searching over the whole database. Our method is based on partitioning the search space by applying the K-means algorithm on the data. Then, similarity search is performed hierarchically starting from the cluster that lies most closely to the query. This procedure aims at reaching the most similar series without searching all clusters. In this work, we propose to reduce the intrinsically high dimensionality of time series prior to clustering by applying a well known dimensionality reduction technique, namely, the Piecewise Aggregate Approximation, for its simplicity and efficiency. Experiments are conducted on twelve real-world and synthetic datasets covering a wide range of applications.
Starting Page	113
Ending Page	118
File Size	341935
Page Count	6
File Format	PDF
ISBN	9780769537832
DOI	10.1109/BCI.2009.22
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-17
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multidimensional systems Discrete Fourier transforms Time series Information retrieval Data mining Clustering Nearest neighbor searches Similarity search Degradation Databases Aggregates Informatics Indexing
Content Type	Text
Resource Type	Article

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