Interval Set Clustering of Web Users with Rough K-Means

Lingras, Pawan; West, Chad

doi:10.1023/b:jiis.0000029668.88665.1a

Cited by 458 publications

(172 citation statements)

References 14 publications

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“…Each object belongs to all clusters with certain degree of belongingness. Rough k-means (RKM) was proposed by Lingras and West [9] by borrowing some of the concepts of rough set theory [13]. Rough Fuzzy c-means algorithm was proposed by Mitra et al, [11] .…”

Section: Soft Rough Fuzzy C-means Algorithm (Srfcm)mentioning

confidence: 99%

“…Wang et al, in their work [19] applied the pixel wise homogeneity and texture features to SVM by training SVM, using the features obtained by preliminary clustering with Fuzzy C Means (FCM) algorithm. Lingras [9] et al, proposed rough k means algorithm for use in clustering of internet users, which was later applied for image segmentation applications. Pradipta Maji and Sankar Pal proposed RFCM, [12] in which they presented that, crisp lower bound and fuzzy boundary of a class, enables efficient selection of cluster prototypes.Freixenet et al, [8] proposed to integrate the information pertaining to region and boundary for colour texture based segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM

R.V.V¹,

Srinivaskumar²

2016

IJCI

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Section: Soft Rough Fuzzy C-means Algorithm (Srfcm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM

R.V.V¹,

Srinivaskumar²

2016

IJCI

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“…In the present paper we devise a rough fuzzy set-based hybrid-clustering approach towards leukocyte segmentation in order to minimize these errors. Fuzzy c-means (FCM) [31] and rough c-means (RCM) [32] algorithms are merged together to develop a hybrid-clustering algorithm. Fuzzy sets have the ability to deal with issues like overlapping patterns, uncertainty, and vagueness.…”

Section: Literature Surveymentioning

confidence: 99%

“…In Rough c-means (RCM) clustering, the idea of standard K-means is extended by visualizing each class as an interval or rough set [32]. A rough set Y is characterized by its lower and upper approximations BY and BY respectively.…”

Section: Rough C-means (Rcm)mentioning

confidence: 99%

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Mohapatra

Patra

Kumar

2012

ISRN Artificial Intelligence

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The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

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“…These modifications make it possible to represent clusters as rough sets [97]. In their work, Lingras established a rough k-means framework and extended the concept of c-means by viewing each cluster as an interval or rough set [69]. Here is a brief summary of his pioneer clustering work.…”

Section: Adaptation Of C-means To Rough Set Theorymentioning

confidence: 99%

Computational Intelligence in Multimedia Processing: Foundation and Trends

Hassanien

Abraham²,

Kacprzyk³

et al. 2008

Studies in Computational Intelligence

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Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.

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Interval Set Clustering of Web Users with Rough K-Means

Cited by 458 publications

References 14 publications

Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM

Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Computational Intelligence in Multimedia Processing: Foundation and Trends

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