21 search results - page 4 / 5 » Categorizing Concepts for Detecting Drifts in Stream |
ICDM
2006
IEEE
14 years 9 days ago
2006
IEEE
When monitoring sensory data (e.g., from a wearable device) the context oftentimes changes abruptly: people move from one situation (e.g., working quietly in their office) to ano...
KDD
2003
ACM
14 years 6 months ago
2003
ACM
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
ICDM
2009
IEEE
13 years 4 months ago
2009
IEEE
Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited...
CICLING
2010
Springer
14 years 1 months ago
2010
Springer
We present an approach for automatic detection of topic change. Our approach is based on the analysis of statistical features of topics in time-sliced corpora and their dynamics ov...
CORR
2004
Springer
13 years 6 months ago
2004
Springer
Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by...