NFmcp @ ECML-PKDD 2018 

7th International Workshop on 

New Frontiers in Mining Complex Patterns 

Amedeo Napoli

Université de Lorraine, CNRS, Inria, LORIA, 54000 Nancy, France

Contributions to Exploratory Knowledge Discovery based on Formal Concept Analysis

Knowledge discovery (KD) in complex datasets and especially the mining of interesting patterns can be read along several dimensions among which data, knowledge and problem-solving dimensions. These dimensions have a direct impact on the whole Knowledge Discovery process, and for guiding the exploration of the data and the pattern spaces. In this presentation, we will discuss Exploratory Knowledge Discovery in the framework of Formal Concept Analysis (FCA) considering the dimensions of data, knowledge and problem-solving.
FCA starts with a binary context and outputs a concept lattice, which can be visualized, navigated and interpreted by human agents, and, as well, processed by software agents. FCA can be extended into Pattern Structures, where objects may have complex descriptions such as numbers, sequences, trees, and graphs. Descriptions can be compared and a pattern concept lattice can be built accordingly.
In this framework, we will detail two important data mining problems, namely the mining of definitions in RDF data (Linked Data) and the search for functional dependencies. The former depends on the discovery of implications within RDF data and can be used to detect missing information and to complete RDF data. The second one is also based on implication discovery and partition pattern structures which can also be applied to biclustering.
We will discuss how pattern structures provide a general basis for solving such data mining problems. Finally, we will conclude by analyzing the benefits of FCA and pattern structures for exploratory knowledge discovery.