Mining Data in Multi-perspectives
نویسنده
چکیده
Knowledge discover in Data bases (KDD) concerns the development of models which allow the representation and organization of the knowledge hidden in data. Machine learning systems are an alternative to automate the KDD process. In particular, incremental concept formation systems construct hierarchical abstract representations from observations (non-classified description of specific entities, events or situations), by recognizing regularities among them. These systems are especially interesting for KDD because they create concepts that allow better understanding of the data.
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