In this talk, I will show that the inclusion of classification structures to the variable reduction and summarization methods can overcome these problems. We call these methods cluster harnessing analyses. First, I talk about the correlation of variables which can measure similarity between the correlation of variables and the correlation of classification structures. In addition, I will show that this correlation can be derived from the dissimilarity of data and from the classification structures for the two fixed variables which we call fuzzy self-organized dissimilarity. Second, I will talk about the variable selection criterion using the fuzzy clustering result. This criterion can show how the dissimilarity of variables at each object can match the given classification as the external information to the data. According to the value of this criterion for each variable, we can select the significant variables. In addition, based on these selected variables, I show how to obtain the data in which the number of objects is larger than the number of variables by exploiting the representation of interval-valued data. Several applications of the proposed cluster harnessing analyses for identifying a classifier of microarray data, which is a typical high dimension low sample-size data, will be demonstrated along with a new definition of marker factor instead of conventional maker gene.