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» Mining for Useful Association Rules Using the ATMS
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ISSTA
2010
ACM
13 years 11 months ago
Learning from 6, 000 projects: lightweight cross-project anomaly detection
Real production code contains lots of knowledge—on the domain, on the architecture, and on the environment. How can we leverage this knowledge in new projects? Using a novel lig...
Natalie Gruska, Andrzej Wasylkowski, Andreas Zelle...
WSDM
2010
ACM
261views Data Mining» more  WSDM 2010»
14 years 5 months ago
Learning Similarity Metrics for Event Identification in Social Media
Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host ...
Hila Becker, Mor Naaman, Luis Gravano
DMKD
2003
ACM
114views Data Mining» more  DMKD 2003»
14 years 28 days ago
Deriving link-context from HTML tag tree
HTML anchors are often surrounded by text that seems to describe the destination page appropriately. The text surrounding a link or the link-context is used for a variety of tasks...
Gautam Pant
WWW
2006
ACM
14 years 1 months ago
Do not crawl in the DUST: different URLs with similar text
We consider the problem of dust: Different URLs with Similar Text. Such duplicate URLs are prevalent in web sites, as web server software often uses aliases and redirections, and...
Uri Schonfeld, Ziv Bar-Yossef, Idit Keidar
ISMIS
2005
Springer
14 years 1 months ago
Learning the Daily Model of Network Traffic
Abstract. Anomaly detection is based on profiles that represent normal behaviour of users, hosts or networks and detects attacks as significant deviations from these profiles. In t...
Costantina Caruso, Donato Malerba, Davide Papagni