Constrained clustering of gene expression profiles
Journal
Proceedings of the Conference on Data Mining and Data Warehouses at the Seventh International Multi-Conference on Information Society
Date Issued
2005
Author(s)
Slavkov, Ivica
Dzeroski, Sasho
Struyf, Jan
Abstract
In this paper a querying environment for analysis of
patient clinical data is presented. The data consists of
two parts: patients’ pathological data and data about
corresponding gene expression levels. The querying
environment includes a generic algorithm for
constructing decision trees, as well as algorithms for
discretizing gene expression levels and for searching
frequent patterns (itemsets). The algorithms are
accessed by means of a query language. The language
can be used to simulate various data mining algorithms,
such as the one developed by Morishita et al. for Itemset
Constrained Clustering.
patient clinical data is presented. The data consists of
two parts: patients’ pathological data and data about
corresponding gene expression levels. The querying
environment includes a generic algorithm for
constructing decision trees, as well as algorithms for
discretizing gene expression levels and for searching
frequent patterns (itemsets). The algorithms are
accessed by means of a query language. The language
can be used to simulate various data mining algorithms,
such as the one developed by Morishita et al. for Itemset
Constrained Clustering.
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