TY - GEN
T1 - Location time-series clustering on optimal sensor arrangement
AU - Yang, Zong Hua
AU - Kao, Hung Yu
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84873346268&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873346268&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2012.29
DO - 10.1109/TAAI.2012.29
M3 - Conference contribution
AN - SCOPUS:84873346268
SN - 9780769549194
T3 - Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
SP - 113
EP - 118
BT - Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
T2 - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012
Y2 - 16 November 2012 through 18 November 2012
ER -