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» Mining Very Large Databases
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ICDE
1999
IEEE
139views Database» more  ICDE 1999»
15 years 11 months ago
Clustering Large Datasets in Arbitrary Metric Spaces
Clustering partitions a collection of objects into groups called clusters, such that similar objects fall into the same group. Similarity between objects is defined by a distance ...
Venkatesh Ganti, Raghu Ramakrishnan, Johannes Gehr...
ICDE
2010
IEEE
750views Database» more  ICDE 2010»
15 years 2 months ago
Efficient and accurate discovery of patterns in sequence datasets
Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require effici...
Avrilia Floratou, Sandeep Tata, Jignesh M. Patel
SSDBM
2010
IEEE
185views Database» more  SSDBM 2010»
14 years 8 months ago
DESSIN: Mining Dense Subgraph Patterns in a Single Graph
Currently, a large amount of data can be best represented as graphs, e.g., social networks, protein interaction networks, etc. The analysis of these networks is an urgent research ...
Shirong Li, Shijie Zhang, Jiong Yang
ISCI
1998
139views more  ISCI 1998»
14 years 9 months ago
A Rough Set Approach to Attribute Generalization in Data Mining
This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification...
Chien-Chung Chan
CIMCA
2005
IEEE
15 years 3 months ago
Mining for Useful Association Rules Using the ATMS
Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and...
Yue Xu, Yuefeng Li