Integrating hidden Markov models and spectral analysis for sensory time series clustering
Integrating hidden Markov models and spectral analysis for sensory time series clustering
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DOI：
10.1109/icdm.2005.82
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发表时间：
2005-11
期刊：
影响因子：
--
通讯作者：
Jie Yin;Qiang Yang
中科院分区：
文献类型：
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作者：
Jie Yin;Qiang Yang
我们提出了一种对从传感器网络获取的多维轨迹数据序列进行聚类的新方法。传感时间序列数据给数据挖掘带来了新的挑战，包括序列长度不均匀、多维性和高噪声水平。我们采用了一种有原则的方法，首先将所有数据转换为等长向量形式，同时尽可能保留时间信息，然后对转换后的数据应用降维和降噪技术，比如谱聚类。对合成数据和真实数据的实验评估表明，我们提出的方法优于针对时间序列数据的标准基于模型的聚类算法。
We present a novel approach for clustering sequences of multi-dimensional trajectory data obtained from a sensor network. The sensory time-series data present new challenges to data mining, including uneven sequence lengths, multi-dimensionality and high levels of noise. We adopt a principled approach, by first transforming all the data into an equal-length vector form while keeping as much temporal information as we can, and then applying dimensionality and noise reduction techniques such as spectral clustering to the transformed data. Experimental evaluation on synthetic and real data shows that our proposed approach outperforms standard model-based clustering algorithms for time series data.