Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

Maji, Pradipta; Pal, Sankar K.

doi:10.1109/tsmcb.2007.906578

Cited by 206 publications

(95 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The diagram represented below describes the division of the system, the processes performed by it and the flow of information across the modules of the system. And the new centroid may be calculated based on the weighting average of the crisp lower approximation and fuzzy boundary for better results [7]. The notion of rough sets was introduced by Pawlak [6] in the year 1982 as an extension of the crisp sets.…”

Section: Methodsmentioning

confidence: 99%

“…Each partition is represented by a set of three parameters, namely, a cluster prototype (centroid), a crisp lower approximation, and a fuzzy boundary. approximation influencing the fuzziness of the final partition may be obtained [7].…”

Section: It Involvesmentioning

confidence: 99%

“…Computation of the centroid can be modified to include the effects of both fuzzy memberships (probabilistic and possibilistic) and lower and upper bounds [7]. In this paper, a generalized hybrid algorithm, termed as rough-fuzzy PCM (RFPCM) is proposed, based on rough and fuzzy sets.…”

Section: Centroid Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques

Swarnalatha¹,

Tripathy²

2012

IJISA

View full text Add to dashboard Cite

Abstract-Detection of affected areas in images is a crucial step in assessing the depth of the affected area for municipal operators. These affected areas in the underground images, which are line images are indicative of the condition of buried infrastructures like sewers and water mains. These images identify affected areas and extract their properties like structures from the images, whose contrast has been enhanced.. . A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques presents a three step method which is a simple, robust and efficient one to detect affected areas in the underground concrete images. The proposed methodology is to use segmentation and feature extraction using structural elements. The main objective for using this model is to find the dimensions of the affected areas such as the length, width, depth and the type of the defects/affected areas. Although human eye is extremely effective at recognition and classification, it is not suitable for assessing defects in images, which might have spread over thousands of miles of image lines. The reasons are mainly fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the affected areas in underground images. A proposal to apply rough fuzzy set theory to compute the lower and upper approximations of the affected area of the image is made in this paper. In this connection we propose to use some concepts and technology developed by Pal and Maji.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: It Involvesmentioning

confidence: 99%

See 1 more Smart Citation

A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques

Swarnalatha¹,

Tripathy²

2012

IJISA

View full text Add to dashboard Cite

show abstract

“…In the article [22], the CGS algorithm was applied on only two Axial-T2 MR images and was not compared previously with any existing clustering methods. In this paper, the proposed methodology is applied on six Axial-T2 MR images and a comparative study is done with PSO [23], K-Means [29] and FCM [30] based segmentation methods with both qualitative and quantitative measurement index (i.e Dunn Index [24,25]). Finally, statistical significance in the performances of the methods has been tested.…”

Section: Related Workmentioning

confidence: 99%

“…In the proposed method, a quantitative measurement index called as Dunn Index [24,25] is used to measure the performance of clustering algorithm. Dunn index is defined as following:…”

Section: Fig 2 An Example Of a Part Of Genotypementioning

confidence: 99%

Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Si¹,

De²,

Bhattacharjee³

2015

IJCA

View full text Add to dashboard Cite

This paper presents Grammatical Swarm based segmentation methodology for lesion detection in brain's magnetic resonance image. In the proposed methodology, images are denoised using median filter at the outset. Secondly, images are segmented using Grammatical Swarm based hard-clustering technique. Finally, lesions are extracted from the segmented images. The proposed methodology is applied on six Axial-T2 magnetic resonance images and compared with Particle Swarm Optimizer, K-Means and FCM based segmentation methods using quantitative performance measurement index. The experimental results show that the proposed methodology statistically outperforms other methods.

show abstract

Introduction to Pattern Recognition and Data Mining

Maji

Pal

2012

Rough‐Fuzzy Pattern Recognition

View full text Add to dashboard Cite

Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices

Cited by 206 publications

References 14 publications

A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques

A Centroid Model for the Depth Assessment of Images using Rough Fuzzy Set Techniques

Grammatical Swarm based Segmentation Methodology for Lesion Segmentation in Brain MRI

Introduction to Pattern Recognition and Data Mining

Contact Info

Product

Resources

About