Automatic ontology generation for data mining using fca and clustering
This work addresses the need for formalized domain representations in data mining, though it appears incremental in its approach.
The paper tackled the problem of automatically generating a fuzzy ontology for data mining by fusing conceptual clustering, fuzzy logic, and Formal Concept Analysis, resulting in optimized space memory and execution time for data exploitation.
Motivated by the increased need for formalized representations of the domain of Data Mining, the success of using Formal Concept Analysis (FCA) and Ontology in several Computer Science fields, we present in this paper a new approach for automatic generation of Fuzzy Ontology of Data Mining (FODM), through the fusion of conceptual clustering, fuzzy logic, and FCA. In our approach, we propose to generate ontology taking in consideration another degree of granularity into the process of generation. Indeed, we suggest to define an ontology between classes resulting from a preliminary classification on the data. We prove that this approach optimize the definition of the ontology, offered a better interpretation of the data and optimized both the space memory and the execution time for exploiting this data.