document: Evolutionary spectral clustering by incorporating temporal smoothness Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams . In evolutionary clustering , a good clustering result should fit the current data well , while simultaneously not deviate too dramatically from the recent history . To fulfill this dual purpose , a measure of temporal smoothness is integrated in the overall measure of clustering quality . In this paper , we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering . For both frameworks , we start with intuitions gained from the well-known k-means clustering problem , and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems . Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts . Furthermore , we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems . Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts .
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