A Partitive Rough Clustering Algorithm

Peters, Georg; Lampart, Martin

doi:10.1007/11908029_68

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…Perers and Lampart17 suggested rough k ‐medoids and Peters18 proposed a rough switching regression model, which—together with the rough k ‐means‐–form a class of rough partitive algorithms.…”

Section: Related Approaches To Rough Clusteringmentioning

confidence: 99%

“…The basic concept of representing a set as lower and upper approximations can be used in a broader context such as clustering. Clustering in relation to rough set theory is attracting increasing interest among researchers 5–20. This paper describes different rough clustering techniques, rough clustering validation measures, and their applications.…”

Section: Introductionmentioning

confidence: 99%

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How To Make Ohmic Contacts to Organic Semiconductors

et al. 2004

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The process of charge injection plays an important role in organic semiconductor devices. We review various experimental techniques that allow injection to be separated from other competing processes, and quantify the injection efficiency of a contact. We discuss the dependence of the injection efficiency on parameters such as the energy barrier at the interface, the carrier mobility of the organic semiconductor, its carrier density (doping level), the presence of mobile ions, and the sample geometry. Based on these findings, we outline guidelines for forming ohmic contacts and present examples of contact engineering in organic semiconductor devices.

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Section: Related Approaches To Rough Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How To Make Ohmic Contacts to Organic Semiconductors

et al. 2004

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“…On the basis of Kaufmanns k ‐medoids and Lingras' rough k ‐means, Peters et al 17 developed a rough k ‐medoids clustering algorithm. An evolutionary version of the rough k ‐medoids was also introduced by Peters et al 15 and applied to synthetic, colon cancer, forest, and control chart data.…”

Section: Rough Partitive Algorithmsmentioning

confidence: 99%

A class of dynamic rough partitive algorithms

Peters

Weber

2011

Int. J. Intell. Syst.

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Partitive algorithms, like cluster algorithms, are frequently used methods in data mining. Most of them are static in the sense that they detect pattern in stable data structures, i.e. the data structure remains unchanged over time. However, many real-life situations are characterized by changing data environments that require an adaptation of the respective algorithms. The retail industry, for example, is frequently faced with changes in its customers' buying behavior. Segments of customers that are buying certain goods may vary within one year or even within one single day. Detecting these changes could provide business opportunities for the respective retailer. For such changing data structures, dynamic clustering algorithms have been developed that adapt to changing environments. This paper discusses a class of rough partitive algorithms and introduces their possible dynamic extensions. C 2011 Wiley Periodicals, Inc.

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“…Clustering in relation to rough set theory is attracting increasing interest among researchers. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] This paper describes…”

Section: Introductionmentioning

confidence: 99%

Rough clustering

Lingras

Peters

2011

WIREs Data Min & Knowl

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Traditional clustering partitions a group of objects into a number of nonoverlapping sets based on a similarity measure. In real world, the boundaries of these sets or clusters may not be clearly defined. Some of the objects may be almost equidistant from the center of multiple clusters. Traditional set theory mandates that these objects be assigned to a single cluster. Rough set theory can be used to represent the overlapping clusters. Rough sets provide more flexible representation than conventional sets, at the same time they are less descriptive than the fuzzy sets. This paper describes the basic concept of rough clustering based on k‐means, genetic algorithms, Kohonen self‐organizing maps, and support vector clustering. The discussion also includes a review of rough cluster validity measures, and applications of rough clustering to such diverse areas as forestry, medicine, medical imaging, web mining, super markets, and traffic engineering. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 64‐72 DOI: 10.1002/widm.16 This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Technologies > Structure Discovery and Clustering

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A Partitive Rough Clustering Algorithm

Cited by 17 publications

References 9 publications

How To Make Ohmic Contacts to Organic Semiconductors

How To Make Ohmic Contacts to Organic Semiconductors

A class of dynamic rough partitive algorithms

Rough clustering

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