A recent-biased clustering algorithm of data stream

Previous data stream clustering algorithms not only computational speed is slow, but also data element of data stream is treated equally. In this paper, a recent-biased clustering algorithm of data stream based on tilted-time window is proposed. First, the algorithm segments sliding window equal in length to form no overlap data blocks. Then extract feature of every data block through Haar wavelet transform, and preserve detail feature of recent data by varying number of wavelet coefficients of data block, namely more recent data block, more wavelet coefficient preserved, and vice versa. Finally, by applying recent-biased distance of data stream, implements the recent-biased clustering algorithm of data stream based on tilted-time window. Remarkably faster computational speed and higher efficient have been achieved by this algorithm. The simulation results validate the efficiency of our algorithm.