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KDD
2002
ACM
171views Data Mining» more  KDD 2002»
11 years 2 days ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
ECAI
2004
Springer
10 years 5 months ago
Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets
Advances in data collection technologies allow accumulation of large and high dimensional datasets and provide opportunities for learning high quality classification and regression...
Kang Peng, Zoran Obradovic, Slobodan Vucetic
ICDE
2008
IEEE
192views Database» more  ICDE 2008»
11 years 1 months ago
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
Mining frequent itemsets from data streams has proved to be very difficult because of computational complexity and the need for real-time response. In this paper, we introduce a no...
Barzan Mozafari, Hetal Thakkar, Carlo Zaniolo
KDD
2004
ACM
285views Data Mining» more  KDD 2004»
10 years 5 months ago
Effective localized regression for damage detection in large complex mechanical structures
In this paper, we propose a novel data mining technique for the efficient damage detection within the large-scale complex mechanical structures. Every mechanical structure is defi...
Aleksandar Lazarevic, Ramdev Kanapady, Chandrika K...
DAGSTUHL
2007
10 years 1 months ago
Subspace outlier mining in large multimedia databases
Abstract. Increasingly large multimedia databases in life sciences, ecommerce, or monitoring applications cannot be browsed manually, but require automatic knowledge discovery in d...
Ira Assent, Ralph Krieger, Emmanuel Müller, T...
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