A Method of Weather Cases Generation Based on Similarity Rough Set

Author

Ji, Sai ; Yuan, Shenfang ; Yue, Jian

Author_Institution

Dept. of Comput. Sci. & Technol., Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China

fYear

2009

fDate

20-22 Sept. 2009

Firstpage

1

Lastpage

4

Abstract

Case selection from weather database is a key step in disaster weather forecasts based on CBR. The selection of representative weather cases without noise and reduces time and space complexity is its essential target. This paper proposes the SRS algorithm based on similarity-based rough set theory. By reducing undirected graph, it can select a reasonable number of the typical cases from a large data set for future case-based reasoning tasks. It also can handle noise and inconsistent data. Experimental result has confirmed the algorithm feasibility and the validity.

Keywords

case-based reasoning; rough set theory; weather forecasting; CBR; SRS algorithm; case-based reasoning; disaster weather forecasts; similarity rough set; undirected graph; weather cases generation; weather database; Computer science; Information retrieval; Information science; Information systems; Materials science and technology; Noise reduction; Rough sets; Set theory; Space technology; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Management and Service Science, 2009. MASS '09. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4638-4

Electronic_ISBN

978-1-4244-4639-1

Type

conf

DOI

10.1109/ICMSS.2009.5302086

Filename

5302086