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» CURE: An Efficient Clustering Algorithm for Large Databases
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124
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CORR
2010
Springer
138views Education» more  CORR 2010»
15 years 3 months ago
Data Stream Clustering: Challenges and Issues
Very large databases are required to store massive amounts of data that are continuously inserted and queried. Analyzing huge data sets and extracting valuable pattern in many appl...
Madjid Khalilian, Norwati Mustapha
ACCV
2010
Springer
14 years 10 months ago
Descriptor Learning Based on Fisher Separation Criterion for Texture Classification
Abstract. This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fish...
Yimo Guo, Guoying Zhao, Matti Pietikäinen, Zh...
132
Voted
VLDB
1999
ACM
159views Database» more  VLDB 1999»
15 years 7 months ago
Aggregation Algorithms for Very Large Compressed Data Warehouses
Many efficient algorithms to compute multidimensional aggregation and Cube for relational OLAP have been developed. However, to our knowledge, there is nothing to date in the lite...
Jianzhong Li, Doron Rotem, Jaideep Srivastava
SAC
2005
ACM
15 years 9 months ago
Mining concept associations for knowledge discovery in large textual databases
In this paper, we describe a new approach for mining concept associations from large text collections. The concepts are short sequences of words that occur frequently together acr...
Xiaowei Xu, Mutlu Mete, Nurcan Yuruk
116
Voted
ADC
2006
Springer
120views Database» more  ADC 2006»
15 years 9 months ago
Approximate data mining in very large relational data
In this paper we discuss eNERF, an extended version of non-Euclidean relational fuzzy c-means (NERFCM) for approximate clustering in very large (unloadable) relational data. The e...
James C. Bezdek, Richard J. Hathaway, Christopher ...