Location time-series clustering on optimal sensor arrangement

Zong Hua Yang, Hung-Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many researches focus on clustering location or time series. In time series data, similarity metric are often used to measure the similar data. Many works use different algorithms to calculate similarity between two subsequences. Also, in location clustering, the well-known algorithm k-means and k-NN propose excellent results, and many works focus on how to increase efficient and accuracy of these algorithms. However, in some cases, we need to consider both similarities between locations and time series. Like greenhouse or wildfire detection, key points in these cases play an important status. This paper addresses how to cluster both location points and time series data together. We propose a new algorithm to consider location and time series together. We further show the algorithm we proposed can solve the problem of real cases well.

Original languageEnglish
Title of host publicationProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
Pages113-118
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan
Duration: 2012 Nov 162012 Nov 18

Publication series

NameProceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012

Other

Other2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
CountryTaiwan
CityTainan
Period12-11-1612-11-18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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