Dealing Big Data using Fuzzy C-Means (FCM) Clustering and Optimizing with Gravitational Search Algorithm (GSA)

Venkat, Ramanan Sridaran; Reddy, K. Satyanarayan

doi:10.1109/icoei.2019.8862673

Cited by 10 publications

(10 citation statements)

References 16 publications

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“…GSA can fulfill the difficulties in achieving precise results. FCM is rectified with GSA; Fig.1 is FCM technique which valuates Fitness to get precise results [16]. Fig.2 GSA process, FCM with GSA finds Cluster Centroids and fitness variance is required.…”

Section: Methodsmentioning

confidence: 99%

“…Partitions nearer to the centroids obtained are given to reduction. Enhanced FCM involved with GSA obtains improved outcomes [16].…”

Section: Methodsmentioning

confidence: 99%

“…[14] Proposes Approach for estimated closest neighbor for matching applying k-means, calculating and comparing these with similar existing approaches for getting good features. The drawbacks in k-means is Hierarchical nature, it produces many divisions and here each instance need to belong to a single group but in [16] FCM an instance can belong to any number of groups as discussed. [15] Proposed Confidential based combined cloud methodology, k-means was used as data retrieving tool to gather essential information which could protect cloud privacy, [18] the main disadvantage of k-means is it fails in determining number of clusters before handling and other disadvantages are all data forced in to a cluster, Clustering models can depend on starting locations of clustering centers, needs k clusters to be determined initially and detects only hyper spherical clusters that are well separated and Sensitive to noise & outlier points.…”

Section: Fig 3 Map-reducing Process [16]mentioning

confidence: 99%

“…[15] Proposed Confidential based combined cloud methodology, k-means was used as data retrieving tool to gather essential information which could protect cloud privacy, [18] the main disadvantage of k-means is it fails in determining number of clusters before handling and other disadvantages are all data forced in to a cluster, Clustering models can depend on starting locations of clustering centers, needs k clusters to be determined initially and detects only hyper spherical clusters that are well separated and Sensitive to noise & outlier points. But [16] FCM has Objective function to select optimum number of clusters as this gives probability value. [17] States that for tailoring FCM to overcome limitations, GSA which is motivated with Gravity principles is applied.…”

Section: Fig 3 Map-reducing Process [16]mentioning

confidence: 99%

“…This improves the demerits of GSA. Map-Reducing deals the process better by getting the better solution [16].…”

Section: Introductionmentioning

confidence: 99%

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Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization

Venkat*,

Reddy

2020

IJRTE

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There is a lot of bulk data which can be efficiently structured using some Clustering mechanism, among these mechanisms Fuzzy C-Means (FCM) Clustering technique is very new and can handle this bulk data logically and in a well precise mode. FCM is a better technique when compared to K-Means as FCM is designed with Fuzzy Concerns. But clustering only cannot give precise outcome, that’s the reason we are involving an Optimization technique for tuning the results and Gravitational Search Algorithm (GSA) Optimization can makes the outcome more precise. GSA is concerned with gravity principles. GSA tailors the defects and transitions into a well structure system and finally FCM will be optimized using GSA. This System is developed with Map-Reduced method. Here in this paper, a discussion is being presented with different existing techniques that were previously used to structure the data and it is discussed how FCM with GSA is better technique when compared to those techniques and some sample Preprocessing Patterns and k-means clustering results are obtained as a first step of research.

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Section: Methodsmentioning

confidence: 99%

“…Partitions nearer to the centroids obtained are given to reduction. Enhanced FCM involved with GSA obtains improved outcomes [16].…”

Section: Methodsmentioning

confidence: 99%

Section: Fig 3 Map-reducing Process [16]mentioning

confidence: 99%

Section: Fig 3 Map-reducing Process [16]mentioning

confidence: 99%

“…This improves the demerits of GSA. Map-Reducing deals the process better by getting the better solution [16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization

Venkat*,

Reddy

2020

IJRTE

Self Cite

View full text Add to dashboard Cite

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Fuzzy Clustering Implementations for Big Data in R

Di Perna,

Ferraro

2024

Advances in Intelligent Systems and Computing

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Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

Ding

Feng

Andreu-Pérez

et al. 2022

Neural Process Lett

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Fuzzy C-Means (FCM) is a common clustering algorithm, and scholars usually use Multiple Population Genetic Algorithm (MPGA) to optimize the clustering centers. Still, MPGA has insufficient global search capabilities and lacks selfadaptability, so the optimized clustering centers are not optimal and eventually tend to converge prematurely. Therefore, this paper proposes an adaptive FCM clustering algorithm, referred to as DMGA-FCM, based on Derived Multi-population Genetic Algorithm (DMGA). The first proposed derivation operator for DMGA-FCM considers the problem of insufficient optimization ability among populations. This operator performs derivative operations on the initial population to improve the algorithm's optimization-seeking capability. At the same time, the adaptive probability fuzzy control operator dynamically adjusts the genetic probability to improve the algorithm adaptability, thereby enhancing the global optimization capability of the DMGA algorithm and avoiding premature convergence problems. Finally, this is integrated into the FCM algorithm. The analysis of simulation experiments and MRI brain map application results shows that DMGA-FCM outperforms other competitive methods in medical imaging segmentation and clustering.

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Dealing Big Data using Fuzzy C-Means (FCM) Clustering and Optimizing with Gravitational Search Algorithm (GSA)

Cited by 10 publications

References 16 publications

Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization

Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization

Fuzzy Clustering Implementations for Big Data in R

Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

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