The KDD process is divided into three main steps: data preparation, data mining, and data interpretation

The figure details data preparation within the KDD process and illustrates our method of data selection guided with domain knowledge. Data relevant to the study are collected from various resources such as genomic variation databases, published pharmacogenomic studies and private datasets. Various operations are applied to these data: cleaning, integration and transformation. Theses operations implies first an instantiation of a knowledge base (1), and second the design of the “initial dataset”(2). In this study, a dataset is defined as a relation between set of objects (rows) and set of attributes (columns). A mapping is then built between objects and attributes of this dataset and the instances from the KB (3). Data selection results from the definition of a subset of instances in the KB (4), allowing the selection of corresponding objects and attributes, with respect to the mapping. This process takes as inputs the initial dataset and the KB is controlled by the domain expert, and yields the “reduced dataset”. Characteristics of the ontology such as subsumption relationships, properties and class descriptions, are used to guide the definition of meaningful instance subsets. These subsets are in turn used for data selection. Data mining algorithms are then applied to the reduced dataset. The results of the mining operation are interpreted in terms of knowledge units that can be eventually integrated into the knowledge base.

Copyright information:

Taken from "Ontology-guided data preparation for discovering genotype-phenotype relationships"

http://www.biomedcentral.com/1471-2105/9/S4/S3

BMC Bioinformatics 2008;9(Suppl 4):S3-S3.

Published online 25 Apr 2008

PMCID:PMC2367630.