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VLDB
2010
ACM
144views Database» more  VLDB 2010»
15 years 4 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
16 years 7 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
15 years 7 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»
16 years 1 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
15 years 4 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, ...