Perspectives on clusteringKing-Sun Fu prizeThe practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fu
Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been ...
Data clustering: 50 years beyond k-means Pattern Recognit. Lett. (2010) L. Wang et al. Robust level set image segmentation via a local correntropy-based k-means clustering Pattern Recognit. (2014) F. Gullo et al. An information-theoretic approach to hierarchical clustering of uncertain data ...
K-means自从在不同的科学领域中由Steinhaus(1956)、Lloyd(1957年提出,1982年发表)、Ball和Hall(1965)以及MacQueen(1967)独立的发现以来,已经有了丰富而多样的历史。即使K-means距离它第一次提出已经过去了50多年,它仍然是聚类中最广泛使用的算法之一。易实现、简单、有效、经验上的成功是这个算法如此受欢迎的主要...
Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666 (2010). Google Scholar Quake, S. R., Wyss-Coray, T., Darmanis, S. & The Tabula Muris Consortium. Transcriptomic characterization of 20 organs and tissues from mouse at single cell resolution creates a ...
Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651–666 (2010). Article Google Scholar Manning, C. D., Raghavan, P. & Schütze, H. Introduction to Information Retrieval (Cambridge University Press, 2008). Jackson, J. E. A User’s Guide to Principal Components (...
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651–666 Article Google Scholar Sacerdoti FD, Katz MJ, Massie ML (2003) Wide area cluster monitoring with Ganglia. In: IEEE International Conference on CLUSTER Computin, pp 289–298 Gold MS, Bentler PM ...
# Perform clustering with a different number of clustersR=range(50,1000,50) KM = (cluster.KMeans(n_clusters=k).fit(data)forkinR) Then determine the error for each case and visualize the data obtained. distance=(k.transform(data)forkinKM) ...
Selecting the correct value of k is an important aspect of k-means clustering. We can make use of the elbow method to pick the appropriate k value. To do this, we run the k-means algorithm on a range of values, e.g., 1 to 15. For each value of k, we compute an average score...
Data clustering: 50 years beyond k-means Pattern Recognit. Lett. (2010) R. Liu et al. Shared-nearest-neighbor-based clustering by fast search and find of density peaks Inf. Sci. (2018) N.C. Sandes et al. Clustering ensembles: a hedonic game theoretical approach Pattern Recognit. (2018)...