Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/1191
Title: Cluster-Based Similarity Search in Time Series
Authors: Karamitopoulos, Leonidas
Evangelidis, Georgios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: similarity search
clustering
time series
data mining
Issue Date: 2009
First Page: 113
Last Page: 118
Volume Title: 2009 Fourth Balkan Conference in Informatics
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.
URI: https://doi.org/10.1109/BCI.2009.22
https://ruomo.lib.uom.gr/handle/7000/1191
ISBN: 978-0-7695-3783-2
Other Identifiers: 10.1109/BCI.2009.22
Appears in Collections:Department of Applied Informatics

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