| About the Book | |||
Data mining has emerged to address the problem of transforming data into useful knowledge. Although most datamining techniques, such as the use of association rules, may substantially reduce the search effort over large datasets, often, theMoreData mining has emerged to address the problem of transforming data into useful knowledge. Although most datamining techniques, such as the use of association rules, may substantially reduce the search effort over large datasets, often, the consequential outcomes surpass the amount of information humanly manageable. On the otherhand, important association rules may be overlooked owing to the setting of the support threshold, which is a verysubjective metric, but rooted in most data mining techniques. This paper presents a study on the effects, in termsof precision and recall, of using a data preparation technique, called SemPrune, which is built on domain ontology.SemPrune is intended for pre- and post-processing phases of data mining. Identifying generalization/specializationrelations, as well as composition/decomposition relations, is the key to successfully applying SemPrune. | |||
