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» CURE: An Efficient Clustering Algorithm for Large Databases
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126
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MM
2004
ACM
99views Multimedia» more  MM 2004»
15 years 9 months ago
Locality preserving clustering for image database
It is important and challenging to make the growing image repositories easy to search and browse. Image clustering is a technique that helps in several ways, including image data ...
Xin Zheng, Deng Cai, Xiaofei He, Wei-Ying Ma, Xuey...
138
Voted
ICNP
1997
IEEE
15 years 7 months ago
TCP behavior with many flows
TCP's ability to share a bottleneck fairly and efficiently decreases as the number of competing flows increases. This effect starts to appear when there are more flows than p...
Robert Morris
126
Voted
DATAMINE
2006
142views more  DATAMINE 2006»
15 years 3 months ago
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized infor...
Hye-Chung Kum, Joong Hyuk Chang, Wei Wang 0010
159
Voted
ICAIL
2005
ACM
15 years 9 months ago
Effective Document Clustering for Large Heterogeneous Law Firm Collections
Computational resources for research in legal environments have historically implied remote access to large databases of legal documents such as case law, statutes, law reviews an...
Jack G. Conrad, Khalid Al-Kofahi, Ying Zhao, Georg...
142
Voted
CIKM
2006
Springer
15 years 5 months ago
Efficiently clustering transactional data with weighted coverage density
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
Hua Yan, Keke Chen, Ling Liu