In the microarray experiments, most of the unsupervised learning processes involve three steps: standardization, defining a dissimilarity measure, and applying a clustering algorithm. We will discuss the issues involved in these steps, and we will propose new methods. A new distance measure that takes into account the time order and the time distance between experiments will be introduced. A clustering algorithm that finds clusters sequentially and that allows for sporadic objects will be discussed.