k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
Efficiency issues of evolutionary k-means
Applied Soft Computing
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
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Many data stream clustering algorithms operate in two well-defined steps: (i) online statistical data collection stage; and (ii) offline macro-clustering stage. The well-known k-means algorithm is often employed for performing the offline macro-clustering step. The conventional k-means algorithm assumes that the number of clusters (k) is defined a priori by the user. Given the difficulty of defining the value of k a priori in real-world problems, we describe a new approach that allows estimating k dynamically from streams with variable number of clusters, which is a common scenario in data with a non-stationary distribution. In addition, we combine our dynamic approach with two different strategies for initializing the centroids during the offline clustering. Analysis of results suggest that, using the dynamic approach, the method k-means++ for centroids initialization present better results.