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2 International Journal of Applied Mathematics and Computer Science

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Ant-based clustering for flow graph mining

Lewicki Arkadiusz, Pancerz Krzysztof

International Journal of Applied Mathematics and Computer Science

2020

Vol. 30, no. 3

561--572

The paper is devoted to the problem of mining graph data. The goal of this process is to discover possibly certain sequences appearing in data. Both rough set flow graphs and fuzzy flow graphs are used to represent sequences of items originally arranged in tables representing information systems. Information systems are considered in the Pawlak sense, as knowledge representation systems. In the paper, an approach involving ant based clustering is proposed. We show that ant based clustering can be used not only for building possible large groups of similar objects, but also to build larger structures (in our case, sequences) of objects to obtain or preserve the desired properties.

Ant-based extraction of rules in simple decision systems over ontological graphs

Pancerz K., Lewicki A., Tadeusiewicz R.

International Journal of Applied Mathematics and Computer Science

2015

Vol. 25, no. 2

377--387

In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominance-based rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems.