Clustering of DNA microarray temporal data based on the autoregressive model

In this paper, we propose to combine linear prediction coefficients from the autoregressive model (AR) and the time series itself as features for the clustering algorithm. The purpose of the use of the AR model is to realize the importance of dynamic modeling of microarray time series data. We define the distance among the time series profiles using the autoregressive model and use the hierarchical clustering and the k-means clustering methods for comparison. The results show that the performance of the clustering DNA microarray time course profile is increased with the linear prediction coefficients in addition to the time series itself used as features.