@INPROCEEDINGS { Rsds4475,
author = {Wang, Guoyin}, 
title = {Introduction to 3DM: Domain-Oriented Data-Driven Data Mining}, 
booktitle = {Proceedings of the Third International Conference on Rough Sets and Knowledge Technology (RSKT'2008)}, 
conference = {Rough Sets and Knowledge Technology}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 5009, 
pages = {25--26}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
month = {May}, 
year = 2008, 
editor = {Wang, Guoying and Li, Tianrui and Grzymala-Busse, Jerzy W. and Miao, Duoqian and Skowron, Andrzej and Yao, Yiyu}, 
issn = {03029743}, 
isbn = {978-3-540-69069-6}, 
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, }, 
}