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» A hybrid approach to mining frequent sequential patterns
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MCS
2002
Springer
15 years 5 months ago
Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach
The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even mo...
Emilio Benfenati, Paolo Mazzatorta, Daniel Neagu, ...
RECOMB
2004
Springer
16 years 6 months ago
Mining protein family specific residue packing patterns from protein structure graphs
Finding recurring residue packing patterns, or spatial motifs, that characterize protein structural families is an important problem in bioinformatics. To this end, we apply a nov...
Jun Huan, Wei Wang 0010, Deepak Bandyopadhyay, Jac...
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 6 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
WSE
2002
IEEE
15 years 11 months ago
Understanding Web Usage for Dynamic Web-Site Adaptation: A Case Study
Every day, new information, products and services are being offered by providers on the World Wide Web. At the same time, the number of consumers and the diversity of their intere...
Nan Niu, Eleni Stroulia, Mohammad El-Ramly
ICDE
2004
IEEE
115views Database» more  ICDE 2004»
16 years 7 months ago
Unordered Tree Mining with Applications to Phylogeny
Frequent structure mining (FSM) aims to discover and extract patterns frequently occurring in structural data, such as trees and graphs. FSM finds many applications in bioinformat...
Dennis Shasha, Jason Tsong-Li Wang, Sen Zhang