HIMA: A Holistic Data Instance Matching Approach

Author

Miao, Jiajia ; Chen, Guoyou ; Li, Aiping ; Yan, Jia ; Jiang, Siyu

Author_Institution

Inst. of Command Autom., PLA Univ. of Sci. & Technol., Nanjing, China

fYear

2010

fDate

25-27 June 2010

Firstpage

5242

Lastpage

5245

Abstract

Considering the consistency of instance level, we come up with a Holistic Data Instance Matching Approach (HIMA). Firstly, we measure the similarity of instances with the algorithm of string distances. HIMA makes use of the clustering algorithm, which it can handle, a large scale of data source holistically. In addition, we use the keyword extracting method, which is based on the maximum entropy model, to get rid of the useless information. The experimental results show that the keyword extracting algorithm can get 70% precision, and the condition probabilistic based algorithm is more precise than the token-based algorithm. HIMA method can achieve 83% accuracy.

Keywords

data analysis; entropy; information retrieval; pattern clustering; string matching; HIMA; clustering algorithm; holistic data instance matching; keyword extracting method; maximum entropy model; string distance; Clustering algorithms; Computational modeling; Computers; Couplings; Data mining; Entropy; Programmable logic arrays; clustering; instance matching; maximum entropy model; string distance;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Control Engineering (ICECE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6880-5

Type

conf

DOI

10.1109/iCECE.2010.1272

Filename

5630513