Sacchi L, Bellazzi R, Larizza C, Magni P, Curk T, Petrovic U, Zupan B
Dipartimento di Informatica e Sistemica, Università di Pavia, Italy.
Stud Health Technol Inform. 2004;107(Pt 2):798-802.
This paper describes a new technique for clustering short time series coming from gene expression data. The technique is based on the labelling of the time series through temporal trend abstractions and a consequent clustering of the series on the basis of their labels. Clustering is performed at three different levels of aggregation of the original time series, so that the results are organized and visualized as a three-levels hierarchical tree. Results on simulated and on yeast data are shown. The technique appears robust and efficient and the results obtained are easy to be interpreted.
本文描述了一种用于对来自基因表达数据的短时间序列进行聚类的新技术。该技术基于通过时间趋势抽象对时间序列进行标记,并基于这些标记对序列进行聚类。聚类是在原始时间序列的三个不同聚合级别上进行的,以便将结果组织并可视化为一个三级层次树。展示了在模拟数据和酵母数据上的结果。该技术显得稳健且高效,所获得的结果易于解释。