Ant-Based Clustering in Delta Episode Information Systems Based on Temporal Rough Set Flow Graphs

• Autorzy: Pancerz K. , Lewicki A. , Tadeusiewicz R. , Warchoł J.
• Języki publikacji: EN

Abstrakty

EN

In the paper, we focus on ant-based clustering time series data represented by means of the so-called delta episode information systems. A clustering process is made on the basis of delta representation of time series, i.e., we are interested in characters of changes between two consecutive data points in time series instead of original data points. Most algorithms use similarity measures to compare time series. In the paper, we propose to use a measure based on temporal rough set flow graphs. This measure has a probabilistic character and it is considered in terms of the Decision Theoretic Rough Set (DTRS) model. To perform ant-based clustering, the algorithm based on the versions proposed by J. Deneubourg, E. Lumer and B. Faieta as well as J. Handl et al. is used.

Słowa kluczowe

EN

information storage; retrieval systems; rough sets; flow graphs; graph theory; time series analysis; computer algorithms

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 128, nr 1-2
• Strony: 143--158
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Pancerz K.

• University of Management and Administration, Akademicka Str. 4, 22-400 Zamość, Poland

autor

Lewicki A.

• University of Information Technology and Management, Sucharskiego Str. 2, 35-225 Rzeszów, Poland

autor

Tadeusiewicz R.

• AGH University of Science and Technology, Mickiewicza Av. 30, 30-059 Kraków, Poland

autor

Warchoł J.

• Medical University of Lublin, Jaczewskiego Str. 4, 20-090 Lublin, Poland

Bibliografia

• [1] Cios, K., Pedrycz, W., Swiniarski, R., Kurgan, L.: Data mining. A knowledge discovery approach, Springer, New York, 2007.
• [2] Das, S., Abraham, A., Konar, A.: Metaheuristic Clustering, Springer-Verlag, Berlin Heidelberg, 2009.
• [3] Deneubourg, J., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting: Robot-like ants and ant-like robots, Proceedings of the First International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 1, MIT Press, Cambridge, MA, 1991.
• [4] Gan, G., Ma, C., Wu, J.: Data Clustering. Theory, Algorithms, and Applications, SIAM, Philadelphia, ASA Alexandria, VA, 2007.
• [5] Handl, J., Knowles, J., Dorigo, M.: Ant-Based Clustering and Topographic Mapping, Artificial Life, 12(1), 2006, 35-62.
• [6] Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants, Proceedings of the Third International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3, MIT Press, Cambridge, MA, 1994.
• [7] Matusiewicz, Z., Pancerz, K.: Rough Set Flow Graphs and Max - * Fuzzy Relation Equations in State Prediction Problems, Rough Sets and Current Trends in Computing (C.-C. Chan, J. W. Grzymala-Busse, W. Ziarko, Eds.), 5306, Springer-Verlag, Berlin Heidelberg, 2008.
• [8] Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36, 2006, 795-805.
• [9] Mitra, S., Pedrycz, W., Barman, B.: Shadowed C-Means: Integrating fuzzy and rough clustering, Pattern Recognition, 43, 2010, 1282-1291.
• [10] Pancerz, K.: Extensions of Dynamic Information Systems in State Prediction Problems: the First Study, in: Proceedings of the 12th International Conference on Information Processing andManagement of Uncertainty in Knowledge-based Systems (IPMU'2008) (L. Magdalena, M. Ojeda-Aciego, J. Verdegay, Eds.), Malaga, Spain, 2008, 101-108.
• [11] Pancerz, K.: Extensions of Multistage Decision Transition Systems: The Rough Set Perspective, in: Man- Machine Interactions (K. Cyran, et al., Eds.), Springer-Verlag, Berlin Heidelberg, 2009, 209-216.
• [12] Pancerz, K.: Some Issues on Extensions of Information and Dynamic Information Systems, in: Foundations of Computational Intelligence (5), vol. 205 of Studies in Computational Intelligence, Springer-Verlag, Berlin Heidelberg, 2009, 79-106.
• [13] Pancerz, K., Lewicki, A., Tadeusiewicz, R.: Ant Based Clustering of Time Series Discrete Data - A Rough Set Approach, in: Swarm, Evolutionary, and Memetic Computing (B. K. Panigrahi, et al., Eds.), vol. 7076 of Lecture Notes in Computer Science, Springer-Verlag, Berlin Heidelberg, 2011, 645-653.
• [14] Pancerz, K., Lewicki, A., Tadeusiewicz, R., Warchoł, J.: Rough Set Flow Graphs and Ant Based Clustering in Classification of Disturbed Periodic Biosignals, in: Proceedings of the Workshop on Concurrency, Specification and Programming (CS&P'2012) (L. Popova-Zeugmann, Ed.), vol. 2, Berlin, Germany, 2012, 269-279.
• [15] Pancerz, K., Paja, W., Szkoła, J., Warchoł, J., Olchowik, G.: A Rule-Based Classification of Laryn- gopathies Based on Spectrum Disturbance Analysis - An Exemplary Study, Proc. of the BIOSIGNALS'2012 (S. Van Huffel, et al., Eds.), Vilamoura, Algarve, Portugal, 2012.
• [16] Pancerz, K., Suraj, Z.: Rough Sets for Discovering Concurrent System Models from Data Tables, in: Rough Computing. Theories, Technologies and Applications (A. Hassanien, et al., Eds.), Information Science Reference, Hershey, 2008, 239-268.
• [17] Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht, 1991.
• [18] Pawlak, Z.: Probability, Truth and Flow Graph, Electronic Notes in Theoretical Computer Science, 82(4), 2003, 1-9.
• [19] Pawlak, Z.: Flow Graphs and Data Mining, in: Transactions on Rough Sets III (J. Peters, A. Skowron, Eds.), Springer-Verlag, Berlin Heidelberg, 2005, 1-36.
• [20] Pawlak, Z., Skowron, A.: Rudiments of rough sets, Information Sciences, 177, 2007, 3-27.
• [21] Pelleg, D., Moore, A. W.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters, Proceedings of the Seventeenth International Conference on Machine Learning (P. Langley, Ed.), Stanford, CA, USA, 2000.
• [22] Skowron, A., Stepaniuk, J., Jankowski, A., Bazan, J. G., Swiniarski, R.: Rough Set Based Reasoning About Changes, Fundamenta Informaticae, 119, 2012, 421-437.
• [23] Smolikova, R., Wachowiak, M.: Aggregation operators for selection problems, Fuzzy Sets and Systems, 131, 2002, 23-34.
• [24] Suraj, Z.: The Synthesis Problem of Concurrent Systems Specified by Dynamic Information Systems, in: Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems (L. Polkowski, A. Skowron, Eds.), Physica-Verlag, Heidelberg, Germany, 1998, 418-448.
• [25] Suraj, Z.: Rough Set Methods for the Synthesis and Analysis of Concurrent Processes, in: Rough Set Methods and Applications (L. Polkowski, et al., Eds.), Springer Verlag, Berlin, 2000, 379-488.
• [26] Szkoła, J., Pancerz, K., Warchoł, J.: Computer Diagnosis of Laryngopathies Based on Temporal Pattern Recognition in Speech Signal, Bio-Algorithms and Med-Systems, 6(12), 2010, 75-80.
• [27] Szkoła, J., Pancerz, K., Warchoł, J.: Recurrent Neural Networks in Computer-Based Clinical Decision Support for Laryngopathies: An Experimental Study, Computational Intelligence and Neuroscience, 2011, 2011, Article ID 289398.
• [28] Warchoł, J.: Speech Examination with Correct and Pathological Phonation Using the SVAN 912AE Analyser (in Polish), Ph.D. Thesis, Medical University of Lublin, 2006.
• [29] Witten, I. H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2005.
• [30] Yao, Y.: Information granulation and approximation in a decision-theoretical model of rough sets, in: Rough- Neuro Computing: Techniques for Computing with Words (L. Polkowski, S. Pal, A. Skowron, Eds.), Springer Verlag, Berlin, 2003, 491-516.