J Intell Inf Syst DOI 10.1007/s10844-015-0358-3 A grid density based framework for classifying streaming data in the presence of concept drift Tegjyot Singh Sethi 1 · Mehmed Kantardzic 2 · Hanquing Hu 1 Received: 26 February 2014 / Revised: 13 April 2015 / Accepted: 13 April 2015 © Springer Science+Business Media New York 2015 Abstract Mining data streams is the process of extracting information from non-stopping, rapidly flowing data records to provide knowledge that is reliable and timely. Streaming data algorithms need to be one pass and operate under strict limitations of memory and response time. In addition, the classification of streaming data requires learning in an envi- ronment where the data characteristics might change constantly. Many of the classification algorithms presented in literature assume a 100 % labeling rate, which is impractical and expensive when data records are rapidly flowing in. In this paper, a new incremental grid density based learning framework, the GC3 framework, is proposed to perform classifica- tion of streaming data with concept drift and limited labeling. The proposed framework uses grid density clustering to detect changes in the input data space. It maintains an evolving ensemble of classifiers to learn and adapt to the model changes over time. The frame- work also uses a uniform grid density sampling mechanism to obtain a uniform subset of samples for better classification performance with a lower labeling rate. The entire frame- work is designed to be one-pass, incremental and work with limited memory to perform any-time classification on demand. Experimental comparison with state of the art concept drift handling systems demonstrate the GC3 frameworks ability to provide high classifica- tion performance, using fewer models in the ensemble and with only 4-6 % of the samples  Tegjyot Singh Sethi t0seth01@louisville.edu Mehmed Kantardzic m0kant01@louisville.edu Hanquing Hu h0hu0004@louisville.edu 1 Data Mining Lab, J.B. Speed School of Engineering, University of Louisville, Louisville, KY, USA 2 Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY, USA