A study of various Fuzzy Clustering Algorithms

Grover, Nidhi

doi:10.17950/ijer/v3s3/310

Cited by 54 publications

(18 citation statements)

References 5 publications

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“…The third step is to date the actual cluster with the specific distance measure and lastly, validate the result. The iterations take place until convergence is achieved [16]. With this given process of Fuzzy C-Means algorithm, changing the distance measure can improve the performance of the said algorithm.…”

Section: Data Miningmentioning

confidence: 99%

“…The process of Fuzzy C-Means clustering stops when the convergence is reached. This means that when the threshold becomes zero, the actual clustering process is finished on clustering [16] and as prescribed by the algorithm threshold use was zero. Tracking the execution time of the program can now be observed along with the behavior of the algorithm when different distance measures and different datasets with high dimensions was applied.…”

Section: Startmentioning

confidence: 99%

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Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

Tolentino

Gerardo

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The problem of mining a high dimensional data includes a high computational cost, a high dimensional dataset composed of thousands of attribute and or instances. The efficiency of an algorithm, specifically, its speed is oftentimes sacrificed when this kind of dataset is supplied to the algorithm. Fuzzy C-Means algorithm is one which suffers from this problem. This clustering algorithm requires high computational resources as it processes whether low or high dimensional data. Netflix data rating, small round blue cell tumors (SRBCTs) and Colon Cancer (52,308, and 2,000 of attributes and 1500, 83 and 62 of instances respectively) dataset were identified as a high dimensional dataset. As such, the Manhattan distance measure employing the trigonometric function was used to enhance the fuzzy c-means algorithm. Results show an increase on the efficiency of processing large amount of data using the Netflix ,Colon cancer and SRCBT an (39,296, 38,952 and 85,774 milliseconds to complete the different clusters, respectively) average of 54,674 milliseconds while Manhattan distance measure took an average of (36,858, 36,501 and 82,86 milliseconds, respectively) 52,703 milliseconds for the entire dataset to cluster. On the other hand, the enhanced Manhattan distance measure took (33,216, 32,368 and 81,125 milliseconds, respectively) 48,903 seconds on clustering the datasets. Given the said result, the enhanced Manhattan distance measure is 11% more efficient compared to Euclidean distance measure and 7% more efficient than the Manhattan distance measure respectively.

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Section: Data Miningmentioning

confidence: 99%

Section: Startmentioning

confidence: 99%

Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

Tolentino

Gerardo

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…of the fuzzy C-partition in FCM to obtain a possibilistic type of membership function, they then proposed the PCM algorithm whose membership values represent the degree of typicality rather than the degree of sharing and as consequence the FCM constraint is eliminated [56].…”

Section: =1mentioning

confidence: 99%

Improvement of CSF, WM and GM Tissue Segmentation by Hybrid Fuzzy – Possibilistic Clustering Model based on Genetic Optimization Case Study on Brain Tissues of Patients with Alzheimer’s Disease

Lazli¹,

Boukadoum²

2018

IJNDC

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Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. However FCM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(CSF) tissues using hybrid clustering process which based on: (1) FCM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) Possibilistic C-Means (PCM) algorithm for volumetric measurements of WM, GM, and CSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer's disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FCM and PCM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high.

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“…Panda et al [6] implemented clustering techniques in such wide areas as medicine, business, engineering systems, and image processing. Grover [7] studied a wide variety of fuzzy clustering methods such as CM, Possibilistic CM, and Fuzzy Possibilistic CM algorithm and reported their advantages and drawbacks. Bora and Gupta [8] conducted a comparative study of the fuzzy and hard clustering methods.…”

Section: Introductionmentioning

confidence: 99%

KC-Means: A Fast Fuzzy Clustering

Atiyah

Mohammadpour

Taheri

2018

Advances in Fuzzy Systems

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A novel hybrid clustering method, named KC-Means clustering, is proposed for improving upon the clustering time of the Fuzzy C-Means algorithm. The proposed method combines K-Means and Fuzzy C-Means algorithms into two stages. In the first stage, the K-Means algorithm is applied to the dataset to find the centers of a fixed number of groups. In the second stage, the Fuzzy C-Means algorithm is applied on the centers obtained in the first stage. Comparisons are then made between the proposed and other algorithms in terms of time processing and accuracy. In addition, the mentioned clustering algorithms are applied to a few benchmark datasets in order to verify their performances. Finally, a class of Minkowski distances is used to determine the influence of distance on the clustering performance.

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A study of various Fuzzy Clustering Algorithms

Cited by 54 publications

References 5 publications

Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

Improvement of CSF, WM and GM Tissue Segmentation by Hybrid Fuzzy – Possibilistic Clustering Model based on Genetic Optimization Case Study on Brain Tissues of Patients with Alzheimer’s Disease

KC-Means: A Fast Fuzzy Clustering

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