Please use this identifier to cite or link to this item:
Title: Recent Advances in Time-Series Data Mining: Similarity Measures & Representations
Authors: Karamitopoulos, Leonidas
Evangelidis, Georgios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Issue Date: 2006
First Page: 295
Last Page: 305
Volume Title: Proceedings of the 1st International Scientific Conference, eRA: The Contribution of Information Technology to Science, Economy, Society and Education, Tripolis, Greece
Abstract: In the last decade there has been an increasing interest in mining time series data since huge amounts are generated by several procedures in almost every domain such as in business, industry, medicine, science etc. Moreover, considering image or video data as time series data, the list of time series databases that need to be mined is expanded. During this period of time, hundreds of papers have been published covering all aspects of time series data mining, namely, dimensionality reduction or representation techniques, indexing, clustering, classification, novelty detection, motif discovery etc. Most of the contributions focus on proposing different dimensionality reduction approaches and providing novel similarity measures in order to deal with the unique characteristics of time series data, specifically, the high dimensionality, the high feature correlation and the large amounts of noise and to improve the performance of the existing data mining techniques. The objective of this paper is to serve as an overview of the most recent advances in the field of time series data mining. Although a general overview is included, the literature review is focused mainly on papers of the last three years.
Appears in Collections:Department of Applied Informatics

Files in This Item:
File Description SizeFormat 
2006_ERA_Karamitopoulos.pdf139,74 kBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons