Domain-oriented data-driven data mining (3DM): simulation of human knowledge understanding

  • Authors:

  • Guoyin Wang

  • Affiliations:

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

  • Venue:
  • WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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 database. Data mining (DM) technology has emerged as a means of performing this discovery. It is a useful tool in many fields such as marketing, 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 should obey in a data mining process? In this paper, we will address these questions and propose our answers based on a conceptual data mining model. Our answer would be "data mining is a process of knowledge transformation". It is consistent with the process of human knowledge understanding. Based on analysis of the user-driven and "data-driven" data mining approaches proposed by many other researchers, a conceptual knowledge transformation model and a conceptual domain-oriented data-driven data mining (3DM) model are proposed. It integrates user-driven data mining and data-driven data mining into one system. Some future works for developing such a 3DM data mining system are proposed.