PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

The new module for rules discovering and visualization for NovoSpark® Visualizer software

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Nowy moduł odkrywania i wizualizacji reguł dla systemu NovoSpark® Visualizer
Języki publikacji
EN
Abstrakty
EN
In this paper we present the new rough sets module for NovoSpark® Visualizer (NV) software. We describe the NV system architecture and the place of the new module in it. We also present the procedure of rough sets analysis with NV software. In addition an example of rules discovering and visualization is provided to evaluate the proposed module. The results show that useful rules are discovered efficiently from the data set.
PL
W artykule zaprezentowano projekt nowego modułu do automatyzacji teorii zbiorów przybliżonych dla oprogramowania NovoSpark® Visualizer (NV). Opisano architekturę systemu oraz wskazano miejsce nowego modułu. Ponadto zaprezentowano przebieg procedury analizy i wizualizacji zbiorów przybliżonych w systemie. Przedstawiono przykład odkrywania i wizualizacji reguł za pomocą opracowanej procedury. W wyniku przeprowadzenia eksperymentu udało się otrzymać szereg użytecznych reguł decyzyjnych.
Słowa kluczowe
Rocznik
Strony
197--200
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
  • University of Szczecin, Institute of IT in Management, 64 Mickiewicza Str., 71-101, Szczecin
autor
  • West Pomieranian Uniersity of Technology,, Department of Multimedia Systems, 49 Żołnierska Str., 70-210, Szczecin
Bibliografia
  • [1] Wlodyka A., Mlynarski R., Ilczuk G., Pilat E., Kargul W. Visualization of Decision Rules – from the Cardiologist's Point of View, Proc. Conference Computers in Cardiology, (2008), 645-648
  • [2] Hahsler M., Chelluboina S., ArulesViz - Visualizing Association Rules, R package version 0.1-1. 10, (2011)
  • [3] Bruzzese D., Davino C., Visual Mining of Association Rules, in Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, Springer-Verlag, (2008), 103-122
  • [4] Unwin A., Hofmann H., Bernt K., The Two Key Plot for Multiple Association Rules Control, Proc. of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, Springer-Verlag, (2001), 472-483
  • [5] Klemettinen M., Mannila H., Ronkainen P., Toivonen H., Verkamo A. I., Finding Interesting Rules from Large Sets of Discovered Association Rules, CIKM, (1994), 401-407
  • [6] Rainsford C. P., Roddick J. F., Visualisation of Temporal Interval Association Rules, Proc. of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents, 2000, 91-96
  • [7] Buono P., Costabile M. F., Visualizing Association Rules in a Framework for Visual Data Mining, From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments, (2005), 221-231
  • [8] Ertek G., Demiriz A., (2006), A Framework for Visualizing Association Mining Results, ISCIS, 593-602
  • [9] Blanchard J., Guillet F., Briand H. Interactive visual exploration of association rules with rule-focusing methodology, Knowledge and Information Systems, 13 (2007), No. 1, 43-75
  • [10] Bastian M., Heymann S., Jacomy M., Gephi: An Open Source Software for Exploring and Manipulating Networks, ICWSM 8 (2009), 361-362
  • [11] Ong K.-H., leong Ong K., Ng W.-K., Lim E.-P., CrystalClear: Active Visualization of Association Rules, ICDM'02 International Workshop on Active Mining, (2002)
  • [12] Wong P.C, Whitney P., Thomas J. Visualizing association rules for text mining, Proc. of the 1999 IEEE symposium on information visualization, IEEE Computer Society, (1999), 120–123
  • [13] Han J., An A., Cercone, N., CViz: An Interactive Visualization System for Rule Induction, LNCS, (2000), 214-226
  • [14] Yang L., Pruning and Visualizing Generalized Association Rules in Parallel Coordinates, Knowledge and Data Engineering, 17 (2005), 60-70
  • [15]Hofmann H, Wilhelm A., Visual comparison of association rules, Comp Stat, 16 (2001), No.3, 399–415
  • [16] Hofmann H., Siebes A., Wilhelm A. F. X., Visualizing Association Rules with Interactive Mosaic Plots, in KDD, (2000), 227-235
  • [17] Blanchard J, Fabrice Guillet, Henri Briand Exploratory Visualization for Association Rule Rummaging, Proc. of the 4th International Workshop on Multimedia Data Mining MDM/KDD (2003), 107-114
  • [18] Ilczuk G., Wakulicz-Deja A., Visualization of Rough Set Decision Rules for Medical Diagnosis Systems, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNCS, Volume 4482 (2007), 371-378
  • [19] Sekhavat Y. A., Hoeber O., Visualizing Association Rules Using Linked Matrix, Graph, and Detail Views, Int. J. of Intelligence Science, (2013), No.3, 34-49
  • [20] Valdes J., Virtual Reality Representation of Information Systems and Decision Rules: An exploratory technique for understanding data and knowledge structure. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing LNCS, 2639, (2003), 615-618
  • [21] Jiang B., Han Ch., Hu X., A finite ranked poset and its application in visualization of association rulet, GrC 2008, 2008.
  • [22] Berrado A., Runger G. C., Using metarules to organize and group discovered association rules, Data Min. Knowl. Disc., 14 (2007), 409-431
  • [23] Berardi M., Appice A., Loglisci C., Leo P. Supporting Visual Exploration of Discovered Association Rules Through Multi- Dimensional Scaling. ISMIS 2006, LNAI 4203 (2006), 369-378.
  • [24] Carson Kai-Sang Leung, Pourang P. Irani, and Christopher L. Carmichael. FIsViz: A Frequent Itemset Visualizer. PAKDD 2008, LNAI 5012 (2008), 644-652
  • [25] Techapichetvanich K., Datta A., VisAR: A New Technique for Visualizing Mined Association Rules, ADMA 2005, LNAI 3584 (2005), 88-95
  • [26] Marghoubi R., Boulmakoul A., Zeitouni K., The Use of the Galois lattice for the extraction and the visualization of the spatial association rules, Signal Processing and Information Technology, (2006), 606-611
  • [27] Yahia S., Nguifo E., Contextual generic association rules visualization using hierarchical fuzzy meta-rules, Proc. of Fuzzy Systems, (2004), No. 1, 227-232
  • [28] Herawan T., Yanto I.T.R., Deris M. M., SMARViz: Soft Maximal Association Rules Visualization, IVIC 2009, LNCS 5857 (2009), 664-674
  • [29] Pilipczuk O., Shamroni D., Podstawowe aspekty tworzenia systemów grafiki kognitywnej, Problemy Zarządzania, 07 (2012), No.10 (3), 248-261
  • [30] Eidenzon D., Shamroni D., Volovodenko V., Method and System for Multidimensional Data Visualization, LAP Lambert Academic Publishing, (2013)
  • [31] Eidenzon D., Pilipczuk O., Multidimensional data visualization, Encyclopedia of Information Science and Technology, IGIGlobal, Hershey, (2014), 1600-1610
  • [32] Pawlak Z., Skowron A., Rudiments of Rough Sets, Inform. Sciences, 177, (2007), No.1, 3-27
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-74447d77-249a-4241-9679-44255e3995a3
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.