Feature-based fuzzy classification for interpretation of mammograms

Iyer, Naresh; Kandel, Abraham; Schneider, M.

doi:10.1016/s0165-0114(98)00175-4

Cited by 56 publications

(27 citation statements)

References 4 publications

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“…2 Data distribution of the Iris flower data set and data distribution of the data set created by Eqs. (7) and (8) …”

Section: Proposal Of Granular Fuzzy C-means Algorithmsmentioning

confidence: 99%

“…Fuzzy Clustering algorithms [2,5,[10][11][12]14] are popular and widely used in different areas of research like pattern recognition [2], data mining [6], classification [8], image segmentation [16,18], data analysis and modeling [3] among others, obtaining good results in these implementations. The popularity of this kind of algorithms is due to the fact that allow a datum to belong to different data clusters into a given data set, the main objective of the fuzzy clustering algorithms are find interesting patterns or group of data that share similar characteristics into a given data set.…”

Section: Introductionmentioning

confidence: 99%

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A New Proposal for a Granular Fuzzy C-Means Algorithm

Rubio

Castillo

2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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Fuzzy clustering algorithms are able to find the centroids and partition matrices, but are predominantly numerical, although each cluster prototype can be considered as a granule of information it continues to be a numeric value, in order to give a similar representation structure data. Granular theory and clustering algorithms can be combined to achieve this goal, resulting in granular prototypes and granular matrices of belonging and a more reflective data structure.

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“…2 Data distribution of the Iris flower data set and data distribution of the data set created by Eqs. (7) and (8) …”

Section: Proposal Of Granular Fuzzy C-means Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Proposal for a Granular Fuzzy C-Means Algorithm

Rubio

Castillo

2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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show abstract

“…Subsequently, extensions of the FCM to different cluster shapes [60]- [62] and criteria [63] plus faster algorithms [64] were developed. A good survey of the main characteristics of the different fuzzy clustering algorithms is given in [61] and [65].…”

Section: F Location Theory and Weber Problemsmentioning

confidence: 99%

Video-on-Demand Network Design and Maintenance Using Fuzzy Optimization

Abadpour

Alfa

Diamond

2008

IEEE Trans. Syst., Man, Cybern. B

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Abstract-Video-on-demand (VoD) is the entertainment source that, in the future, will likely overtake regular television in many aspects. Although many companies have deployed working VoD services, some aspects of the VoD should still undergo further improvement in order for it to reach to the foreseen potentials. An important aspect of a VoD system is the underlying network in which it operates. According to the huge number of customers in this network, it should be carefully designed to fulfill certain performance criteria. This process should be capable of finding optimal locations for the nodes of the network as well as determining the content that should be cached in each one. While this problem is categorized in the general group of network optimization problems, its specific characteristics demand a new solution to be sought for it. In this paper, which is inspired by the successful use of fuzzy optimization in similar problems in other fields, a fuzzy objective function that is heuristically shown to minimize the communication cost in a VoD network is derived while also controlling the storage cost. Then, an iterative algorithm is proposed to find a locally optimal solution to the proposed objective function. Capitalizing on the unrepeatable tendency of the proposed algorithm, a heuristic method for picking a good solution from a bundle of solutions produced by the proposed algorithm is also suggested. This paper includes a formal statement of the problem and its mathematical analysis. In addition, different scenarios in which the proposed algorithm can be utilized are discussed.Index Terms-Fuzzy optimization, network design, video-ondemand (VoD).

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“…An image can be categorized in different feature spaces, & the FCM algorithm categorizes the image by grouping similar data points in the feature space into clusters. This clustering is accomplished by iteratively minimizing a cost function that is reliant on the distance of the pixels to the cluster centers in the feature domain (Chuang et al, 2006;Bezdek et al, 1993;Iyer et al, 2000). An image"s pixels are very much correlated, i.e.…”

Section: Introductionmentioning

confidence: 99%

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

2017

IJAERD

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Feature-based fuzzy classification for interpretation of mammograms

Cited by 56 publications

References 4 publications

A New Proposal for a Granular Fuzzy C-Means Algorithm

A New Proposal for a Granular Fuzzy C-Means Algorithm

Video-on-Demand Network Design and Maintenance Using Fuzzy Optimization

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

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