• Corpus ID: 6278891

Some methods for classification and analysis of multivariate observations

  title={Some methods for classification and analysis of multivariate observations},
  author={J. MacQueen},
The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends… 

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Implementation of the k-means Method for Single and Multi - Dimensions

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Method of Classification through Normal Distribution Approximation Using Estimating the Adjacent and Multidimensional Scaling

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Variable Selection in K-Means Clustering via Regularization

A new method of K-means clustering is proposed to detect irrelevant variables to the cluster structure and achieves the purpose of calculating variable weights using an entropy regularization method.

Improved Clustering with Augmented k-means

Augmented k-means frequently outperforms k-Means by more accurately classifying observations into known clusters and / or converging in fewer iterations, which can be valuable when the data exhibit many characteristics of real datasets such as heterogeneity, non-sphericity, substantial overlap, and high scatter.

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Model Based Penalized Clustering for Multivariate Data

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Asymptotics for The k-means

A new concept called clustering consistency is proposed, which is more appropriate than the previous criterion consistency for the clustering methods and has lower clustering error rates and is more robust to small clusters and outliers than existing k -means methods.

A Comparison of Latent Class, K-Means, and K-Median Methods for Clustering Dichotomous Data

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Automation of Data Clusters based on Layered HMM

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On Grouping for Maximum Homogeneity

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Comparison of Experiments

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Hierarchical Grouping to Optimize an Objective Function

Abstract A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for

Note on Grouping

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  • L. DubinsL. J. Savage
  • Mathematics
    Proceedings of the National Academy of Sciences of the United States of America
  • 1965
(X1 + .. . + Xn) 2> + (Aul + ...+ Ang) + a!(V1 + ...+ Vn) (1) is less than 1/(1 + aO3). This bound is sharp. Two lemmas, neither of which are difficult to verify, are used in the proof. The first of

Data analysis in the social sciences: what about the details?

  • G. Ball
  • Sociology
    AFIPS '65 (Fall, part I)
  • 1965
This paper attempts to demonstrate that there exists a class of techniques more suitably oriented toward the capabilities of the digital computer than are conventional analytic statistical techniques, and maintains that these techniques are capable of considering details in social sciences data, that is, relating the individuals described in the data.

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This new edition continues the story of psychology with added research and enhanced content from the most dynamic areas of the field--cognition, gender and diversity studies, neuroscience and more,

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