A graph-based algorithm for cluster detection

被引：10

作者：

Foggia, Pasquale ^{[1
,3
]}

Percannella, Gennaro ^{[2
]}

Sansone, Carlo ^{[1
]}

Vento, Mario ^{[2
]}

机构：

[1] Univ Naples Federico II, Dipartimento Informat & Sistemist, I-80125 Naples, Italy

[2] Univ Salerno, Dipartimento Ingn Informaz & Ingn Elettr, I-84084 Fisciano, SA, Italy

[3] Univ Naples Federico II, Dept Comp Sci, I-80125 Naples, Italy

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2008年 / 22卷 / 05期

关键词：

graph-based cluster detection; fuzzy c-means; minimum spanning tree; benchmarking;

D O I：

10.1142/S0218001408006557

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A comparison with other four state-of-the-art graph-based algorithms was also provided, demonstrating the effectiveness of the proposed approach.

引用

页码：843 / 860

页数：18

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