Location time-series clustering on optimal sensor arrangement

Zong Hua Yang, Hung-Yu Kao

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
頁面113-118
頁數6
DOIs
出版狀態Published - 2012 12月 1
事件2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 - Tainan, Taiwan
持續時間: 2012 11月 162012 11月 18

出版系列

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

Other

Other2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
國家/地區Taiwan
城市Tainan
期間12-11-1612-11-18

All Science Journal Classification (ASJC) codes

  • 人工智慧

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