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AUSDM
2007
Springer
145views Data Mining» more  AUSDM 2007»
14 years 1 months ago
Discovering Frequent Sets from Data Streams with CPU Constraint
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recogniz...
Xuan Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vince...
DATAMINE
1999
152views more  DATAMINE 1999»
13 years 7 months ago
Discovery of Frequent DATALOG Patterns
Discovery of frequent patterns has been studied in a variety of data mining settings. In its simplest form, known from association rule mining, the task is to discover all frequent...
Luc Dehaspe, Hannu Toivonen
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...
ICDM
2002
IEEE
114views Data Mining» more  ICDM 2002»
14 years 16 days ago
Online Algorithms for Mining Semi-structured Data Stream
In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, w...
Tatsuya Asai, Hiroki Arimura, Kenji Abe, Shinji Ka...
HAIS
2011
Springer
12 years 11 months 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...