TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model
Nasraoui, O.
Uribe, C.C.
Coronel, C.R.
Gonzalez, F.
Dept. of Electr. & Comput. Eng., The Univ. of Memphis, TN, USA;
Abstract
Artificial immune system (AIS) models hold many promises in the field of unsupervised learning. However, existing models are not scalable, which makes them of limited use in data mining. We propose a new AIS based clustering approach (TECNO-STREAMS) that addresses the weaknesses of current AIS models. Compared to existing AIS based techniques, our approach exhibits superior learning abilities, while at the same time, requiring low memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to other approaches is expected to be its ease of adaptation to the dynamic environment that characterizes several applications, particularly in mining data streams. We illustrate the ability of the proposed approach in detecting clusters in noisy data sets, and in mining evolving user profiles from Web clickstream data in a single pass. TECNO-STREAMS adheres to all the requirements of clustering data streams: compactness of representation, fast incremental processing of new data points, and clear and fast identification of outliers.
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