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VLDB
2010
ACM
144views Database» more  VLDB 2010»
13 years 7 months ago
Methods for finding frequent items in data streams
The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in d...
Graham Cormode, Marios Hadjieleftheriou
CCS
2009
ACM
14 years 9 months ago
The union-split algorithm and cluster-based anonymization of social networks
Knowledge discovery on social network data can uncover latent social trends and produce valuable findings that benefit the welfare of the general public. A growing amount of resea...
Brian Thompson, Danfeng Yao
ESANN
2004
13 years 10 months ago
Data Mining Techniques on the Evaluation of Wireless Churn
This work focuses on one of the most critical issues to plague the wireless telecommunications industry today: the loss of a valuable subscriber to a competitor, also defined as ch...
Jorge Ferreira, Marley B. R. Vellasco, Marco Aur&e...
ICDM
2009
IEEE
172views Data Mining» more  ICDM 2009»
14 years 3 months ago
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon...
ACL
2009
13 years 7 months ago
Mining Bilingual Data from the Web with Adaptively Learnt Patterns
Mining bilingual data (including bilingual sentences and terms1 ) from the Web can benefit many NLP applications, such as machine translation and cross language information retrie...
Long Jiang, Shiquan Yang, Ming Zhou, Xiaohua Liu, ...