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ICDM
2006
IEEE
149views Data Mining» more  ICDM 2006»
16 years 26 days ago
Pattern Mining in Frequent Dynamic Subgraphs
Graph-structured data is becoming increasingly abundant in many application domains. Graph mining aims at finding interesting patterns within this data that represent novel knowl...
Karsten M. Borgwardt, Hans-Peter Kriegel, Peter Wa...
ICDM
2005
IEEE
168views Data Mining» more  ICDM 2005»
16 years 13 days ago
Discovering Frequent Arrangements of Temporal Intervals
In this paper we study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals. We assume that the database consists of sequences of even...
Panagiotis Papapetrou, George Kollios, Stan Sclaro...
PAKDD
2010
ACM
203views Data Mining» more  PAKDD 2010»
15 years 11 months ago
Finding Itemset-Sharing Patterns in a Large Itemset-Associated Graph
Both itemset mining and graph mining have been studied independently. Here, we introduce a novel data structure, which is an unweighted graph whose vertices contain itemsets. From ...
Mutsumi Fukuzaki, Mio Seki, Hisashi Kashima, Jun S...
ALT
2010
Springer
15 years 8 months ago
Contrast Pattern Mining and Its Application for Building Robust Classifiers
: The ability to distinguish, differentiate and contrast between different data sets is a key objective in data mining. Such ability can assist domain experts to understand their d...
Kotagiri Ramamohanarao
ICML
2005
IEEE
16 years 7 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan