The X-Alter Algorithm: A Parameter-Free Method of Unsupervised Clustering

被引：3

作者：

Laloe, Thomas ^{[1
]}

Servien, Remi ^{[2
]}

机构：

[1] Univ Nice Sophia Antipolis, Nice, France

[2] INRA, UMR Toxalim, Toulouse, France

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2013年 / 12卷 / 01期

关键词：

Clustering; quantization; K-means; free-parameter algorithm;

D O I：

10.22237/jmasm/1367381580

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Using quantization techniques, Laloe (2010) defined a new clustering algorithm called Alter. This L-1-based algorithm is shown to be convergent but suffers two major flaws. The number of clusters, K, must be supplied by the user and the computational cost is high. This article adapts the X-means algorithm (Pelleg & Moore, 2000) to solve both problems.

引用

页码：90 / 102

页数：13

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