Title :
A new classification mining model based on the data warehouse
Author :
Zhang, Su-lan ; Zhang, Ji-fu
Author_Institution :
Dept. of Comput. Sci. & Eng., Taiyuan Heavy Machinery Inst., China
Abstract :
In this paper, we develop a new classification mining model by merging rough set theory and concept lattice theory according to the data features in the data warehouse. Rough set is a very useful tool which can analysis uncertain and vague data and can be used to classify data with the specific upper and lower approximate set. Concept lattice, an efficient formal analysis tool, can classify the data through the relation of concept intension and concept extension. When faced the integrated, diversity, or capacity data in the data warehouse, merging the two theory will improve the efficiency and reliability of the classification knowledge, which can effectively guide the decision-making management.
Keywords :
classification; data mining; data warehouses; decision making; knowledge based systems; rough set theory; classification knowledge reliability; classification mining; concept lattice theory; data warehouse; decision-making management; formal analysis tool; rough set theory; Computer science; Data engineering; Data mining; Data warehouses; Decision making; Lattices; Machine learning; Machinery; Merging; Set theory;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1264464
