基于AR模型的动态模糊聚类算法

与传统的硬划分聚类相比,模糊聚类算法(以FCM为例)对数据的比例变化具有鲁棒性,能够更准确地反映数据点与类中心的实际关系,目前已得到广泛应用.然而对于时序基因表达数据来说,传统的聚类算法往往不能充分利用到数据中时间上的动态关联信息.因此可以在模糊聚类算法的基础上引入自回归(AR)模型,将时序基因表达数据作为一组时间序列进行动态的聚类分析.这样不仅可以充分利用到时序基因表达数据的内部自相关性,并且可以进一步利用隶属度函数对AR模型的预测过程进行模糊化调整,从而得到更为理想的聚类结果.

Dynamic fuzzy clustering based on AR model

Compared with conventional hard partition clustering algorithms,fuzzy clustering algorithms(for example,FCM) are robust to the scaling transformation of a dataset.So they have been used widely because real relationship among samples and cluster cen-ters can be reflected better.However,they cannot make full use of the important dynamic information in time-course gene expression data.Accordingly,autoregressive(AR) model can be introduced into fuzzy clustering algorithm,which can analyze a time-course gene expression data as a set of time series dynamically.In this way,the important dynamic information in time-course gene expression data is used adequately.And the forecast processes in AR model is adjusted using the corresponding membership functions,such that better clustering results for time-course gene expression data is obtained.