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» Discovering Frequent Closed Itemsets for Association Rules
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DKE
2002
218views more  DKE 2002»
13 years 7 months ago
Computing iceberg concept lattices with T
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suit...
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas P...
KDD
2005
ACM
127views Data Mining» more  KDD 2005»
14 years 1 months ago
Mining closed relational graphs with connectivity constraints
Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In this kind of graph, connectivity becomes critical in identif...
Xifeng Yan, Xianghong Jasmine Zhou, Jiawei Han
ADC
2003
Springer
182views Database» more  ADC 2003»
14 years 28 days ago
CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent item sets is computationally the most expens...
Yudho Giri Sucahyo, Raj P. Gopalan
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
14 years 8 months ago
An inductive database prototype based on virtual mining views
We present a prototype of an inductive database. Our system enables the user to query not only the data stored in the database but also generalizations (e.g. rules or trees) over ...
Élisa Fromont, Adriana Prado, Bart Goethals...
AI
2003
Springer
14 years 28 days ago
Efficient Mining of Indirect Associations Using HI-Mine
Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has be...
Qian Wan, Aijun An