DocumentCode :
3627017
Title :
Using Novel IR Measures to Learn Optimal Cluster Structures for Web Information Retrieval
Author :
Martin Mehlitz;Jerome Kunegis;Sahin Albayrak
Author_Institution :
Technical University Berlin, DAI-Labor, 10587 Berlin, Germany
fYear :
2007
Firstpage :
312
Lastpage :
316
Abstract :
The Internet is a vast resource of information. Unfortunately, finding and accessing this information is often a very cumbersome task even with existing information platforms. Searching on the WWW suffers from the fact that almost every word is ambiguous to a certain degree in the information-rich environment of the Internet. Clustering search results is a way to solve this problem. This paper demonstrates how to employ novel Information Retrieval measures to derive optimal parametrizations for a cluster algorithm.
Keywords :
"Information retrieval","Clustering algorithms","Size measurement","Internet","Distortion measurement","Intelligent structures","World Wide Web","Data visualization","Web search","Testing"
Publisher :
ieee
Conference_Titel :
Web Intelligence, IEEE/WIC/ACM International Conference on
Print_ISBN :
0-7695-3026-5;978-0-7695-3026-0
Type :
conf
DOI :
10.1109/WI.2007.42
Filename :
4427109
Link To Document :
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