A Location Based Text Mining Approach for Geospatial Data Mining

Author

Lee, Chung-Hong ; Yang, Hsin-Chang ; Wang, Shih-Hao

Author_Institution

Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan

fYear

2009

fDate

7-9 Dec. 2009

Firstpage

1172

Lastpage

1175

Abstract

In this paper, we describe a location based text mining approach to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones. We first mapped documents into corresponding zones by adaptive affinity propagation (adaptive AP) clustering technique, and then framed maximize zones by means of simplified fuzzy ARTMAP (SFAM) and support vector machines (SVM) methods. Also, we compared our experimental results with the baseline approaches of self-organizing maps (SOM) and learning vector quantization (LVQ) methods. The preliminary results show that our platform framework has the potential for geospatial data mining.

Keywords

data mining; fuzzy set theory; geophysics computing; pattern clustering; self-organising feature maps; support vector machines; text analysis; SVM; adaptive AP clustering technique; adaptive affinity propagation; geospatial data mining; geospatial features; learning vector quantization; location based text mining approach; self-organizing maps; simplified fuzzy ARTMAP; support vector machines; text classification; Data mining; Information management; Information retrieval; Multimedia databases; Ontologies; Self organizing feature maps; Support vector machine classification; Support vector machines; Text mining; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on

Conference_Location

Kaohsiung

Print_ISBN

978-1-4244-5543-0

Type

conf

DOI

10.1109/ICICIC.2009.23

Filename

5412429