Abstract An algorithm based on immune principle, named IUMicro, is proposed to cluster uncertain data streams. IUMicro applies a dynamically updated immune model to adapt to the data streams. An effective B-cell feature vector and updating strategy are used to collect statistical information of data streams on line by this model. To choose the optimal candidate cluster for each increasing tuple in the data stream, IUMicro defines a probability radius of a B-cell’s recognition zone to address both uncertainty and distance metric. The offline clustering is an arbitrary-shape unsupervised clustering based on immune B-cells’ spatial relationship between regions. The experimental results show that IUMicro effectively suppresses noise and gains better clustering quality at a high processing speed.
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2012, Vol. 25 
