Introduction to 3DM: Domain-Oriented Data-Driven Data Mining
Recent advances 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 large-scale knowledg
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Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R. China 2 School of Information Science and Technology, Southwest Jiaotong University, Chengdu 600031, P.R. China [email protected] Abstract. Recent advances 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 large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. 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 [2]. In this talk, some basic issues of data mining are addressed. 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 should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms. Keywords: Data mining, machine learning, rough set, data driven, domain driven, domain oriented.
This work is partially supported by National Natural Science Foundation of P. R. China under Grants No.60573068 and No.60773113, Program for New Century Excellent Talents in University (NCET), Natural Science Foundation of Chongqing, and Science & Technology Research Program of the Municipal Education Committee of Chongqing of China (No.060517).
G. Wang et al. (Eds.): RSKT 2008, LNAI 5009, pp. 25–26, 2008. c Springer-Verlag Berlin Heidelberg 2008
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References 1. Wu, X.D., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 Algorithms in Data Mining. Knowledge and Information Systems 14(1), 1–37 (2008) 2. Wang, G.Y.: Domain-Oriented Data-Driven Data Mining (3DM): Simulation of Human Knowledge Understanding. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (e
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