document: Mining high dimensional data for classifier knowledge We present in this paper the problem of discovering sets of attribute-value pairs in high dimensional data sets that are of interest not because of co-occurrence alone , but due to their value in serving as cores for potential classifiers of clusters . We present our algorithm in the context of a gene-expression dataset . Gene expression data , in most situations , is insufficient for clustering algorithms and any statistical inference because for 6000 + genes , typically only 10s and at most 100s of data points become available . It is difficult to use statistical techniques to design a classifier for such immensely under-specified data . The observed data , though statistically , insufficient contains some information about the domain . Our goal is to discover as much information about all potential classifiers as possible from the data and then summarize this knowledge . This summarization provides insights into the composition of potential classifiers . We present here algorithms and methods for mining a high dimensional data set , exemplified by a gene expression data set , for mining such information .
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