3DM: Domain-oriented Data-driven Data Mining

  • Authors:

  • Guoyin Wang;Yan Wang

  • Affiliations:

  • (Correspd.) Institute of Computer Science and Technology, Chongqing University, of Posts and Telecommunications, Chongqing, 400065, P.R.China. wanggy@cqupt.edu.cn/ wangyan@lut.cn;Institute of Computer Science and Technology, Chongqing University, of Posts and Telecommunications, Chongqing, 400065, P.R.China. wanggy@cqupt.edu.cn/ wangyan@lut.cn

  • Venue:
  • Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
  • Year:
  • 2009

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Abstract

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.