Ant-based clustering for flow graph mining

• Autorzy: Lewicki Arkadiusz , Pancerz Krzysztof

Identyfikatory

DOI

10.34768/amcs-2020-0041

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

possibly certain sequences; flow graph; rough set; fuzzy set; ant based clustering

PL

graf przepływu danych; zbiór przybliżony; zbiór rozmyty

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2020
• Tom: Vol. 30, no. 3
• Strony: 561--572
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Lewicki Arkadiusz

• Chair of Applied Information Systems, University of Information Technology and Management in Rzeszów, Sucharskiego 2, 35-225 Rzeszów, Poland

autor

Pancerz Krzysztof

• Institute of Computer Science, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland

Bibliografia

• [1] Bazan, J.G., Buregwa-Czuma, S. and Jankowski, A.W. (2013). A domain knowledge as a tool for improving classifiers, Fundamenta Informaticae 127(1–4): 495–511.
• [2] Deneubourg, J., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C. and Chrétien, L. (1991). The dynamics of collective sorting: Robot-like ants and ant-like robots, Proceedings of the 1st International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 1, Paris, France, pp. 356–365.
• [3] Dong, G. and Pei, J. (2007). Sequence Data Mining, Springer-Verlag, New York, NY.
• [4] Duch, W., Kucharski, T., Gomuła, J. and Adamczak, R. (1999). Machine Learning Methods in Analysis of Psychometric Data: Application to Multiphasic Personality Inventory MMPI-WISKAD, CMK, Toruń, (in Polish).
• [5] Ellson, J., Gansner, E.R., Koutsofios, E., North, S.C. and Woodhull, G. (2004). Graphviz and Dynagraph—Static and dynamic graph drawing tools, in M. Jünger and P. Mutzel (Eds), Graph Drawing Software, Springer, Berlin/Heidelberg, pp. 127–148.
• [6] Ford, L.R. and Fulkerson, D. (2010). Flows in Networks, Princeton University Press, Princeton, NJ.
• [7] Handl, J., Knowles, J. and Dorigo, M. (2006). Ant-based clustering and topographic mapping, Artificial Life 12(1): 35–62.
• [8] Huang, K.-Y. and Chang, C.-H. (2008). Efficient mining of frequent episodes from complex sequences, Information Systems 33(1): 96–114.
• [9] Klement, E.P., Mesiar, R. and Pap, E. (2000). Triangular Norms, Springer, Dordrecht.
• [10] Kumar, P., Kumar, P., Krishna, P.R. and Raju, S.B. (2011). Pattern Discovery Using Sequence Data Mining: Applications and Studies, IGI Global, Hershey, PA.
• [11] Lumer, E. and Faieta, B. (1994). Diversity and adaptation in populations of clustering ants, Proceedings of the 3rd International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3, Brighton, UK, pp. 501–508.
• [12] Mannila, H., Toivonen, H. and Verkamo, A. (1997). Discovering frequent episodes in event sequences, Data Mining and Knowledge Discovery 1(3): 259–289.
• [13] Marwala, T. (2013). Introduction to Economic Modeling, Springer, London.
• [14] Mieszkowicz-Rolka, A. and Rolka, L. (2006). Flow graphs and decision tables with fuzzy attributes, in L. Rutkowski et al. (Eds), Artificial Intelligence and Soft Computing (ICAISC 2006), Springer, Berlin/Heidelberg, pp. 268–277.
• [15] Mitsa, T. (2010). Temporal Data Mining, CRC Press, Boca Raton, FL.
• [16] Nichols, D. (2011). Essentials of MMPI-2 Assessment, John Wiley, Hoboken, NJ.
• [17] Pancerz, K. (2015). On selected functionality of the classification and prediction software system (CLAPSS), Proceedings of the International Conference on Information and Digital Technologies (IDT’2015), Zilina, Slovakia, pp. 278–285.
• [18] Pancerz, K. (2016). Paradigmatic and syntagmatic relations in information systems over ontological graphs, Fundamenta Informaticae 148(1–2): 229–242.
• [19] Pancerz, K., Lewicki, A. and Tadeusiewicz, R. (2015). Ant-based extraction of rules in simple decision systems over ontological graphs, International Journal of Applied Mathematics and Computer Science 25(2): 377–387, DOI: 10.1515/amcs-2015-0029.
• [20] Pancerz, K., Lewicki, A., Tadeusiewicz, R. and Warchoł, J. (2013). Ant-based clustering in delta episode information systems based on temporal rough set flow graphs, Fundamenta Informaticae 128(1–2): 143–158.
• [21] Pancerz, K., Paja, W., Sarzyński, J. and Gomuła, J. (2018). Determining importance of ranges of MMPI scales using fuzzification and relevant attribute selection, Procedia Computer Science 126: 2065–2074.
• [22] Pancerz, K., Paja, W., Wrzesień, M. and Warchoł, J. (2012). Classification of voice signals through mining unique episodes in temporal information systems: A rough set approach, Proceedings of the 21st International Workshop on Concurrency, Specification and Programming (CS&P 2012), Vilamoura, Algarve, Portugal, pp. 280–291.
• [23] Parpinelli, R.S., Lopes, H.S. and Freitas, A.A. (2002). An ant colony algorithm for classification rule discovery, in H.A. Abbass et al. (Eds), Data Mining: A Heuristic Approach, IGI Global, Hershey, PA, pp. 191–208.
• [24] Pawlak, Z. (1991). Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht.
• [25] Pawlak, Z. (2005). Flow graphs and data mining, in J. Peters and A. Skowron (Eds), Transactions on Rough Sets III, Springer-Verlag, Berlin/Heidelberg, pp. 1–36.
• [26] Tadeusiewicz, R. (2015). Neural networks as a tool for modeling of biological systems, Bio-Algorithms and Med-Systems 11(3): 135–144.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).