Titlebook: Advanced Data Mining and Applications; Third International Reda Alhajj,Hong Gao,Osmar R. Za?ane Conference proceedings 2007 Springer-Verla

討厭

https://doi.org/10.1007/978-3-658-40601-1stream mining. Existing algorithms exploit either bottom-up or top-down processing strategy to solve this problem, whereas we propose a novel combination of these two strategies. Based on this strategy and a devised compact data structure, we implement our algorithm. It is theoretically proved to ha

松軟無(wú)力

Lisa-Marie Pilz,Tobias Prill,Claudia Kalischn the objective function. Such an addition leads to multiple iterations in the E-step. Besides, the clustering result depends mainly on the choice of the spatial coefficient, which is used to weigh the penalty term but is hard to determine a priori. Furthermore, it may not be appropriate to assign a

countenance

Peter Cornelius,Gert-Holger Klevenow (WSNs). The distributed and online learning for target classification is significant for highly-constrained WSNs. This paper presents a collaborative target classification algorithm for image recognition in WSNs, taking advantages of the collaboration for the data mining between multi-sensor nodes.

thalamus

Advanced Data Mining and Applications978-3-540-73871-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

chastise

https://doi.org/10.1007/978-3-540-73871-8Attribut; Bayesian networks; Business-Intelligence; Fusion; algorithms; bioinformatics; classification; cor

Congregate

Deject

采納

織布機(jī)

陶醉

Lisa-Marie Pilz,Tobias Prill,Claudia Kalischiant of NEM using varying coefficients, which are determined by the correlation of explanatory attributes inside the neighborhood. Our experimental results on real data sets show that it only needs one iteration in the E-step and consequently converges faster than NEM. The final clustering quality is also better than NEM.