TKDE
2008
13 years 11 months ago
2008
Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the compl...
TKDE
2008
13 years 11 months ago
2008
We present a new adaptive and energy-efficient broadcast model to support flexible responses to client queries. Clients do not have to request documents by name, since they may kno...
TKDE
2008
13 years 11 months ago
2008
In this paper, we present a new tree mining algorithm, DRYADEPARENT, based on the hooking principle first introduced in DRYADE. In the experiments, we demonstrate that the branchin...
TKDE
2008
13 years 11 months ago
2008
Data stream management systems need to control their resources adaptively since stream characteristics and query workload may vary over time. In this paper we investigate an approa...
TKDE
2008
13 years 11 months ago
2008
Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does n...
TKDE
2008
13 years 11 months ago
2008
UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classificati...
TKDE
2008
13 years 11 months ago
2008
Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns ident...
TKDE
2008
13 years 11 months ago
2008
We propose a novel approach based on predictive quantization (PQ) for online summarization of multiple time-varying data streams. A synopsis over a sliding window of most recent en...
TKDE
2008
13 years 11 months ago
2008
Semisupervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depends on the type of supervision and also...
TKDE
2008
13 years 11 months ago
2008
The multicampaign assignment problem is a campaign model to overcome the multiple-recommendation problem that occurs when conducting several personalized campaigns simultaneously. ...