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* k-means++选择初始中心的方式可以为WCSS目标提供可证明的上限。
* k-means++选择初始中心的方式可以为WCSS目标提供可证明的上限。
* [[过滤算法 filtering algorithm]]使用[[kd树 kd-trees]]来提高每个k-means的步长。<ref>{{cite journal |last1=Kanungo |first1=Tapas |last2=Mount |first2=David M. |authorlink2=David Mount |authorlink3=Nathan Netanyahu |last3=Netanyahu |first3=Nathan S. |last4=Piatko |first4=Christine D.|author4-link=Christine Piatko |last5=Silverman |first5=Ruth |last6=Wu |first6=Angela Y. |year=2002 |title=An efficient ''k''-means clustering algorithm: Analysis and implementation |url=http://www.cs.umd.edu/~mount/Papers/pami02.pdf |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=24 |issue=7 |pages=881–892 |doi=10.1109/TPAMI.2002.1017616 |accessdate=2009-04-24 }}</ref>
* [[过滤算法 filtering algorithm]]使用[[kd树 kd-trees]]来提高每个k-means的步长。<ref>{{cite journal |last1=Kanungo |first1=Tapas |last2=Mount |first2=David M. |authorlink2=David Mount |authorlink3=Nathan Netanyahu |last3=Netanyahu |first3=Nathan S. |last4=Piatko |first4=Christine D.|author4-link=Christine Piatko |last5=Silverman |first5=Ruth |last6=Wu |first6=Angela Y. |year=2002 |title=An efficient ''k''-means clustering algorithm: Analysis and implementation |url=http://www.cs.umd.edu/~mount/Papers/pami02.pdf |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=24 |issue=7 |pages=881–892 |doi=10.1109/TPAMI.2002.1017616 |accessdate=2009-04-24 }}</ref>
* 一些方法尝试使用三角形不等式来加快每个k-means步骤。<ref name="phillips2">{{Cite book |title=Acceleration of ''k''-Means and Related Clustering Algorithms |volume=2409 |last=Phillips |first=Steven J. |date=2002-01-04 |publisher=Springer Berlin Heidelberg |isbn=978-3-540-43977-6 |editor-last=Mount |editor-first=David M. |series=Lecture Notes in Computer Science |pages=166–177 |doi=10.1007/3-540-45643-0_13 |editor-last2=Stein |editor-first2=Clifford |chapter=Acceleration of K-Means and Related Clustering Algorithms }}</ref><ref name="elkan2">{{Cite conference |last=Elkan |first=Charles |year=2003 |title=Using the triangle inequality to accelerate ''k''-means |url=http://www-cse.ucsd.edu/~elkan/kmeansicml03.pdf |booktitle=Proceedings of the Twentieth International Conference on Machine Learning (ICML) }}</ref><ref name="hamerly22">{{Cite journal |title=Making ''k''-means even faster |last=Hamerly |first=Greg |citeseerx=10.1.1.187.3017 }}</ref><ref name="hamerly32">{{cite book |last1=Hamerly |first1=Greg |last2=Drake |first2=Jonathan |date=2015 |title=Accelerating Lloyd's algorithm for ''k''-means clustering |journal=Partitional Clustering Algorithms |pages=41–78 |doi=10.1007/978-3-319-09259-1_2 |isbn=978-3-319-09258-4 }}</ref>
* 一些方法尝试使用三角形不等式来加快每个k-means步骤。<ref name="phillips2">{{Cite book |title=Acceleration of ''k''-Means and Related Clustering Algorithms |volume=2409 |last=Phillips |first=Steven J. |date=2002-01-04 |publisher=Springer Berlin Heidelberg |isbn=978-3-540-43977-6 |editor-last=Mount |editor-first=David M. |series=Lecture Notes in Computer Science |pages=166–177 |doi=10.1007/3-540-45643-0_13 |editor-last2=Stein |editor-first2=Clifford |chapter=Acceleration of K-Means and Related Clustering Algorithms }}</ref><ref name="elkan2">{{Cite conference |last=Elkan |first=Charles |year=2003 |title=Using the triangle inequality to accelerate ''k''-means |url=http://www-cse.ucsd.edu/~elkan/kmeansicml03.pdf |booktitle=Proceedings of the Twentieth International Conference on Machine Learning (ICML) }}</ref><ref name="hamerly22">{{Cite journal |title=Making ''k''-means even faster |last=Hamerly |first=Greg |citeseerx=10.1.1.187.3017 }}</ref><ref name="hamerly32">{{cite book |last1=Hamerly |first1=Greg |last2=Drake |first2=Jonathan |date=2015 |title=Accelerating Lloyd's algorithm for ''k''-means clustering |journal=Partitional Clustering Algorithms |pages=41–78 |doi=10.1007/978-3-319-09259-1_2 |isbn=978-3-319-09258-4 }}</ref><ref name="hamerly22" /><ref>{{Cite journal |last=Drake |first=Jonathan |date=2012 |title=Accelerated ''k''-means with adaptive distance bounds |url=http://opt.kyb.tuebingen.mpg.de/papers/opt2012_paper_13.pdf |journal=The 5th NIPS Workshop on Optimization for Machine Learning, OPT2012 }}</ref>
* 通过在集群之间交换点来逃避局部最优。<ref name="hartigan19792" />
* 通过在集群之间交换点来逃避局部最优。<ref name="hartigan19792" />
* [[球形k均值聚类 Spherical k-means clustering]]算法适用于文本数据。<ref>{{Cite journal |last1=Dhillon |first1=I. S. |last2=Modha |first2=D. M. |year=2001 |title=Concept decompositions for large sparse text data using clustering |journal=Machine Learning |volume=42 |issue=1 |pages=143–175 |doi=10.1023/a:1007612920971 |doi-access=free }}</ref>
* [[球形k均值聚类 Spherical k-means clustering]]算法适用于文本数据。<ref>{{Cite journal |last1=Dhillon |first1=I. S. |last2=Modha |first2=D. M. |year=2001 |title=Concept decompositions for large sparse text data using clustering |journal=Machine Learning |volume=42 |issue=1 |pages=143–175 |doi=10.1023/a:1007612920971 |doi-access=free }}</ref>