Online pattern recognition in subsequence time series clustering
One of the open issues in the context of subsequence time series clustering is online pattern recognition. There are different fields in this clustering such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. Among these fields pattern recogn...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/12985/1/Online_Pattern_recognition.pdf http://eprints.um.edu.my/12985/ |
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| Summary: | One of the open issues in the context of subsequence time
series clustering is online pattern recognition. There are different fields in this clustering such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. Among these fields pattern recognition is one the essential concept. To implement the idea of online pattern recognition, we choose sequences of ECG data as a subsequence time series data. Additionally, using ECG data can help to interpret heart activity for
finding heart diseases. This paper will offer a way to generate online pattern recognition in subsequence time series clustering in order to have a runtime results. |
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