Discovering of interesting regions in spatial data sets using supervised clustering

Discovering of interesting regions in spatial data sets using supervised clustering (2006)

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by Christoph F. Eick , Wei Ding , Jing Wang , Rachsuda Jiamthapthaksin , Dan Jiang

Venue:	In the 10th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD’06
Citations:	9 - 9 self

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Datum	Value	Source
TITLE	Discovering of interesting regions in spatial data sets using supervised clustering	INFERENCE
AUTHOR NAME	Christoph F. Eick	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of Houston	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas 77204-3010, U.S.A	SVM HeaderParse 0.2
AUTHOR NAME	Wei Ding	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of Houston	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas 77204-3010, U.S.A	SVM HeaderParse 0.2
AUTHOR NAME	Jing Wang	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of Houston	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas 77204-3010, U.S.A	SVM HeaderParse 0.2
AUTHOR NAME	Rachsuda Jiamthapthaksin	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of Houston	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas 77204-3010, U.S.A	SVM HeaderParse 0.2
AUTHOR NAME	Dan Jiang	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Computer Science, University of Houston	SVM HeaderParse 0.2
AUTHOR ADDR	Houston, Texas 77204-3010, U.S.A	SVM HeaderParse 0.2
ABSTRACT	Finding interesting documents on the Internet has been major focus of recent research and commercial projects. For example, GOOGLE became famous for building search engines that can efficiently navigate through millions of documents and return a ranked set of documents based on user interests and user feedback. Earth scientists are interested to have similar capabilities to search for interesting regions on the planet earth based on knowledge stored in multiple databases. The Data Mining and Machine Learning Group of the University of Houston (UH-DMML) is dedicated to developing technologies that can satisfy this very need. The focus of this report is to describe in details what techniques, tools, and methodologies we have to offer to scientists who face challenging region discovery problems and who are looking for searchengine style capabablities to find interesting subregions on the planet earth. Data mining has been identified as a key technology to automate the extraction of interesting, useful, but implicit patterns in large spatial datasets. In particular, we are interested in assisting scientists in finding interesting	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In the 10th European Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD’06	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	2 found	ParsCit 1.0