A framework for mining evolving trends in Web data streams using dynamic learning and retrospective validation

Incremental clustering of dynamic data streams using connectivity based representative points

“…Unfortunately, no unsupervised method for cluster extraction is provided, the method instead requiring manual inspection of generated two-and three-dimensional plots. Nasraoui et al [47,46] proposed a clustering technique based on an artificial immune system in which incoming data points are presented to a network of artificial white blood cells (b-cells) that mimic the adaptive mutation observed in biological immune systems. The algorithm classifies incoming data points based on the similarity of each point to existing bcells and each cell's radius of influence.…”

The component search—two-steps optimal algorithm for data-mining in dynamic environments

“…Simultaneously, estimation of works has been discussed in terms of system lifecycle evaluation and estimation attributes (Daskalaki, Kopanas, Goudara, & Avouris, 2003). Additionally, the classifi cation and clustering domains, such as visualization, web data search, position clustering, and graphs classifi cation, have been extensively discussed (Chang & Ding, 2005;Coenen & Leng, 2007;Su & Wang, 2000;Nasraoui, Rojas & Cardona, 2006) In the fi eld of model construction, A GA algorithm based to model A bankruptcy prediction model (Kim & Han, 2003), an artifi cial neural network based to predict subsidence (Ambrozic & Turk, 2003), and the use of biblio-mining frameworks to generate A usage-based forecasting rule (Nicholson, 2006). And, in dynamic system model construction include GA algorithm, an artifi cial neural network, and Fuzzy set, etc, which used to construct the dynamic system model Obviously, when searching for patterns, the CS is A good and an eff ective algorithm (Su &Wang, 2000).…”

The Mahalanobis–Taguchi system – Neural network algorithm for data-mining in dynamic environments

“…Simultaneously, estimation of works has been discussed in terms of system lifecycle evaluation and estimation attributes (Daskalaki, Kopanas, Goudara, & Avouris, 2003). Additionally, the classification and clustering domains, such as visualization, web data search, position clustering, and graphs classification, have been extensively discussed (Chang & Ding, 2005;Coenen & Leng, 2007;Das & Datta, 2007;Nasraoui, Rojas, & Cardona, 2006).…”