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Data mining highly multiple time series of astronomical observations

Huang, Fuchun (2004) Data mining highly multiple time series of astronomical observations. In: Data mining V: data mining, text mining and their business application. Zanasi, A and Ebecken, N. F. F and Brebbia, C. A, eds. WIT Press, Southampton, UK, pp. 375-382.

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Abstract

This is a case study of data mining a large data set of astronomical interest. Our first concern is the outliers apparently existing in the data set. We used a robust method to do curve fitting and identify outliers, and estimated the occurrence intensity of outliers. We find that the occurrence intensity of outliers varies considerably over time. Besides, we designed a test which led to rejection of the hypothesis that all observation series are independent of each other. Combining this fact with our estimation of the occurrence intensity of outliers we believe there are common factors transiently acting on many series of observations. Additionally, we analyse gaps in time series and summarise simple but possibly interesting characteristics of data from a methodological viewpoint of data mining.

Item Type: Book Section
ISBN: 1-85312-722-9
Additional Information:

This paper has been presented at The Data Mining Conference in 2004 was held in Malaga, Spain.

Uncontrolled Keywords: data mining, highly multiple time series, loess, MACHO project, nonparametric curve fitting, outliers
Subjects: RFCD Classification > 240000 Physical Sciences
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: Ms Phung T Tran
Date Deposited: 12 Nov 2008 04:50
Last Modified: 01 Aug 2011 03:37
URI: http://vuir.vu.edu.au/id/eprint/1624
DOI: 10.2495/DATA040361
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Citations in Scopus: 0 - View on Scopus

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