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» A New Algorithm for Mining Sequential Patterns
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ISCI
2008
116views more  ISCI 2008»
13 years 7 months ago
Discovery of maximum length frequent itemsets
The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often ...
Tianming Hu, Sam Yuan Sung, Hui Xiong, Qian Fu
KDD
2005
ACM
147views Data Mining» more  KDD 2005»
14 years 1 months ago
Combining proactive and reactive predictions for data streams
Mining data streams is important in both science and commerce. Two major challenges are (1) the data may grow without limit so that it is difficult to retain a long history; and (...
Ying Yang, Xindong Wu, Xingquan Zhu
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 8 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
14 years 8 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han
KDD
2005
ACM
160views Data Mining» more  KDD 2005»
14 years 8 months ago
Optimizing time series discretization for knowledge discovery
Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the temporal ...
Alfred Ultsch, Fabian Mörchen