Affiliations: Institute of Computer Science and Technology, Chongqing
University, of Posts and Telecommunications, Chongqing, 400065, P.R.China.
[email protected]; [email protected]
Note: [] Address for correspondence: Institute of Computer Science and
Technology, Chongqing University, of Posts and Telecommunications, Chongqing,
400065, P.R.China
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.
Keywords: Domain-oriented, Data-driven, Data Mining
