Abstract
In this paper, it designs an improved sequential clustering approach, which compensates for shortcomings in existing algorithms. This method uses bisecting k-means clustering framework and reduces the computing time through adding the cosine similarity comparison when sequences can not satisfy the pruning condition, while the accuracy is still in an acceptable range.
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Liu, Y., Gao, B., Zhang, X. (2011). An Improved Sequential Clustering Algorithm. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_58
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DOI: https://doi.org/10.1007/978-3-642-23881-9_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23880-2
Online ISBN: 978-3-642-23881-9
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