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» An Iterative Method for Mining Frequent Temporal Patterns
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KDD
2003
ACM
156views Data Mining» more  KDD 2003»
14 years 9 months ago
Fast vertical mining using diffsets
A number of vertical mining algorithms have been proposed recently for association mining, which have shown to be very effective and usually outperform horizontal approaches. The ...
Mohammed Javeed Zaki, Karam Gouda
KDD
2007
ACM
137views Data Mining» more  KDD 2007»
14 years 9 months ago
Characterising the difference
Characterising the differences between two databases is an often occurring problem in Data Mining. Detection of change over time is a prime example, comparing databases from two b...
Jilles Vreeken, Matthijs van Leeuwen, Arno Siebes
TIME
2008
IEEE
14 years 3 months ago
Time Aware Mining of Itemsets
Frequent behavioural pattern mining is a very important topic of knowledge discovery, intended to extract correlations between items recorded in large databases or Web acces logs....
Bashar Saleh, Florent Masseglia
ICDM
2006
IEEE
137views Data Mining» more  ICDM 2006»
14 years 2 months ago
Mining Complex Time-Series Data by Learning Markovian Models
In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of ...
Yi Wang, Lizhu Zhou, Jianhua Feng, Jianyong Wang, ...
HAIS
2011
Springer
13 years 4 days ago
Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm
A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifie...
Stephen G. Matthews, Mario A. Góngora, Adri...