3DM: Domain-oriented Data-driven Data Mining

• Autorzy: Wang G. , Wang Y.
• Języki publikacji: EN

Abstrakty

EN

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics[1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

Słowa kluczowe

EN

domain-oriented; data-driven; data mining

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2009
• Tom: Vol. 90, nr 4
• Strony: 395--426
• Opis fizyczny: bibliogr. 41 poz., tab., wykr.

Twórcy

autor

Wang G.

autor

Wang Y.

• Institute of Computer Science and Technology Chongqing University, of Posts and Telecommunications Chongqing, 400065, P.R.China,

Bibliografia

• [1] Wang, Y.X.: On Cognitive Informatics. Brain and Mind, A Transdisciplinary Journal of Neuroscience and Neurophilosophy,Vol.4, No.2,151-167,2003.
• [2] Y.Y.Yao, Zhong, N., Zhao,Y.:A three-layered conceptual framework of data mining, In Workshop Proceedings Foundations of Data Mining ICDM 2004,Brighton,UK, 205-212.
• [3] Y.Y. Yao:A step towards the foundations of data mining,Data Mining and Knowledge Discovery: Theory, Tools, Technology V,Orlando, Florida, USA, April 21-22, 2003,Dasarathy, B.V.(ed), The International Society for Optical Engineering, 254-263,2003.
• [4] Peng, Y., Kou, G., Shi, Y., Chen, Zh.X.:A Systemic Framework for the Field of Data Mining and Knowledge Discovery, Workshop Proceedings Foundations of Data Mining ICDM2006,Hongkong,China,Dec,18-22,2006.
• [5] S.Ohsuga:Knowledge Discovery as Translation,Studies in Computational Intelligence,2005,6,3-19.
• [6] T.Y.Lin, S. Ohsuga, Proceedings of IEEE ICDM'02 Workshop on Foundation of Data Mining and Knowledge Discoery,2002.
• [7] T.Y.Lin, X.H.Hu, S.Ohsuga, C.J.Liau, Proceedings of IEEE ICDM'03 Workshop on Foundation of New Directions in Data Mining,2003.
• [8] T.Y. Lin, Proceedings of IEEE ICDM'04 Workshop on Foundation of Data Mining,San Jose State,2004.
• [9] W.Frawley,G.Piatetsky-Shapiro,C. Matheus:Knowledge Discovery in Databases: An Overviews,AI Magazine,1992,13,213-228.
• [10] Cao, L., Lin, L., Zhang, C.Q.: Domain-driven In-depth Pattern Discovery, A practical methodology, [Research Report], Faculty of Information Technology, University of Technology, Sydney, Australia, June 2005.
• [11] Zhang, C. Q., Cao, L.:Domain-DrivenDataMining: Methodologies and Applications, Advances in Intelligent IT - Active Media Technology 2006, IOS Press, 13-16.
• [12] Zhao,Y., Y. Y. Yao:Interactive Classification Using a Granule Network, Proceedings of the 4th IEEE Internation Conf on Cognitive Informatics, Irvine, USA, 2005, 250-259.
• [13] P. Kuntz, F. Guillet, R. Lehn, H. Briand: A User-Driven Process for Mining Association Rules, LNCS1910, 2000, 483-489.
• [14] J. Han, L. Lakshmanan, R. Ng: Constraint-Based, Multidimensional Data Mining, IEEE Computer, 1999, 32(8),46-50.
• [15] J. Patrick, D. Palko, R. Munro,M. Zappavigna:User Driven Example-Based Training for Creating Lexical Knowledgebases, Australasian Natural Language Processing Workshop, Canberra, Australia, 2002, 17-24.
• [16] A. Dorado,W. Pedrycz, E. Izquierdo:User-Driven Fuzzy Clustering: On the Road to Semantic Classification, LNCS3641, 2005, 421-430.
• [17] Zhao, J.,Wang, G. Y.:Research on System Uncertainty Measures Based on Rough Set Theory, Rough Set and Knowledge Technology, Volume 4062/2006, 227-232.
• [18] T. Mollestad, A. Skowron.: A Rough Set Framework for Data Mining of Propositional Default Rules, ISMIS' 96,Zakopane, Poland, pp.9-13, June 1996.
• [19] Wang, G.Y., He, X.:Initiative Learning Algorithm Based on Rough Set,Data Mining and Knowledge Discovery: Theory, Tools, and Technology,Volume 5098/2003, 94-102.
• [20] Wang, G.Y., He, X.:A self-Learning Model under Uncertain Conditions, Journal of Software(in Chinese).2003,14(6),1096-1102.
• [21] Pawlak,Z.:Rough set,International Journal of Computer and Information Sciences,1982,11(5),341-356.
• [22] Wang, G.Y.: Rough set theory and knowledge acquisition, Xi'an: Press of Xi'an Jiaotong University, Xi'an, 2001.
• [23] Tom M. Mitchell:Machine Learning, The McGraw-Hill Companies, Inc.1997.
• [24] Hong J. R., DingM. F., Li X. Y.,Wang L.W.: A new algorithm of decision tree induction, Journal of Software, 1995, 18(6),470-474.
• [25] Quinlan J. R., Discovering rules from large collections of examples: a case study,Expert systems in the Micro electronic Age, Edinburgh University Press, Michie D, ed.,1979.
• [26] M. Bramer: Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains, Intelligent Data Engineering and Automated Learning - IDEAL 2002(eds. H.Yin et al.), Springer-Verlag, 2002, 7-12.
• [27] Ganter B,Wille R: Formal concept analysisSpringer,1999.
• [28] Fu, H.Y., Fu, H.G., et al.: A Comparative Study of FCA-Based Supervised Classification Algorithms, Proceedings of the Second International Conference on Formal Concept Analysis, Sydney, Australia, February 23-26,2004,313-320.
• [29] Carpineto, C., Romano, G.: Galois: An order-theoretic approach to conceptual clustering, Proceedings of the Tenth International Conference on Machine Learning, University of Massachusetts, Amherst, MA, USA, June 27-29,1993,33-40.
• [30] Sahami, M.: Learning Classification Rules Using Lattices. Proceedings of the Eighth European Conference on Machine Learning, Heraclion, Crete, Greece, April 25-27,1995,343-346.
• [31] Engelbert Mephu-Nguifo:Galois Lattice: A Framework for Concept Learning. Design, Evaluation and Refinement, Proceedings of the Sixth International Conference on Tools with Artificial Intelligence, New Orleans, Louisiana, USA, November 6-9,1994,461-467.
• [32] Xie, Zh.P., Liu Z.T.: Research on Classifier Based on Lattice Structure,Proceeding of conference on Intelligent Information Processing, 16# World Computer congress 2000, Aug 21-25,Beijing, China,333-338.
• [33] Anamika G, Naveen K, Vasudha B: Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis, Proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, July 9-11, 2005,11-20.
• [34] Blake C., Keogh E., Merz C:UCI repository of machine learning databases, Technical report, University of California, Department of Information and Computer Science, Irvine, CA.(1998) http://www.ics.uci.edy/ mlearn/MLRepository.html.
• [35] Hou, L. J.,Wang, G. Y.,Wu, Y., Nie, N: Discretization in Rough Set Theory, Chinese J. of Computer Science, 2000, 27(12), 89-94.
• [36] Wang, G. Y., Liu, F., Wu, Y.: Generating Rules and Reasoning under Inconsistencies, 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, Japan, 2000, 2536-2541.
• [37] Zhang, W.X., Liao, X. F. Wu, Z. F.: An Incomplete Data analysis Approach Based on Rough set Theory, Pattern Recognition and Artificial Intelligence, 2003, 16(2): 158-162.
• [38] Nguyen H. S., Skowron A: Quantization of Real Value Attributes-Rough Set and Boolean reasoning Approach, Proc of the Second Joint conference on Information sciences, 1995,34-37.
• [39] Qinlan, J. R.: Simpliying decision trees,International Journal of Man- Machine studies, 1987, 27(3), 221-234.
• [40] Apio Elomma, Matti K: An Analysis of Reduced Error Pruning, Journal of Artificial Intelligence Research, 15, 2001,163-187.
• [41] Windeatt T., Ardeshir G: Tree Pruning for Output Coded Ensembles, International Conference Pattern Recognition, ICPR02, 2002, 92-95.