Fuzzy K-means Algorithm Research Articles

A fuzzy K-Means algorithm based on Fisher distribution for the identification of rock discontinuity sets

Rock discontinuities significantly impact the mechanical and hydraulic behavior of rock masses. A crucial aspect of rock engineering involves classifying discontinuities with similar orientations into groups. For this purpose, clustering algorithms, such as K-Means and Fuzzy K-Means (FKM), have been employed. However, the outcomes of these algorithms are influenced by the selection of initial cluster centers. This paper proposes an improved FKM algorithm to automatically identify rock discontinuity sets based on the Fisher distribution (FFKM). The algorithm uses the Fisher distribution to generate and select appropriate initial cluster centers. The performance of FFKM was initially validated using a published data set, and its results were compared with other clustering methods commonly used for grouping discontinuities. Results demonstrated the superior performance of FFKM over the FKM algorithm, comparable to other methods. Subsequently, the proposed algorithm was employed to analyze a fracture data set sampled at an open-pit iron mine. The FFKM facilitated identifying the correct number of sets and produced results consistent with the fracture sets observed in the field. Finally, the algorithm was verified using an artificial discontinuity data set, and the results demonstrated that the method correctly identified the number of sets and provided discontinuity sets similar to the original data set. The FFKM algorithm offers significant advantages: it maintains the essential characteristics of the FKM algorithm, effectively addresses the challenge of selecting suitable initial cluster centers, requires only the expected number of discontinuity sets as an input parameter, processes data within an acceptable computation time, serves as a tool for defining the number of discontinuity sets, and mitigates the drawbacks of the stereographic projection method.

Fuzzy C-Means and K-Means Methods Comparison for the Detection of Diabetes

The necessity for precise and effective disease detection techniques has increased due to the rising incidence of diabetes. The main objective of this study is to assess how well the fuzzy C-Means and K-Means clustering algorithms detect diabetes. Based on pertinent medical data, the study attempts to examine how well these two clustering approaches identify cases of diabetes. For testing, a dataset with a variety of health and diagnostic indicator variables was used. Metrics including sensitivity, specificity, accuracy, and F1-score were used to evaluate the detection performance of the Fuzzy C-Means and K-Means algorithms that were used to cluster the dataset. Based on several evaluation criteria, the results show that both clustering approaches have promising potential for diabetes identification. However, their performance varies. This study sheds light on the advantages and disadvantages of clustering algorithms and advances our understanding of their suitability for diabetes identification. Improved diagnosis precision and early diabetes management intervention could result from more optimization and validation of these algorithms

Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches

Cluster analysis can be used to organize samples and generate ideas regarding the multivariate geochemistry of given dataset. Traditional clustering techniques have the drawbacks of high computational complexity and poor adaptability to big data. Hence, there has recently been much focus on creating better clustering algorithms. Although many clustering algorithms have been applied, and some produce notable clustering results, the performance efficiency of algorithms is often highly dependent on the values the user chooses for the parameters. Currently, density-based spatial clustering of applications with noise clustering (DBSCAN) is widely utilized in image processing, bioinformatics, and social network analysis owing to its ability to detect clusters of various shapes. Even though partitional clustering techniques may be effective when the number of clusters K is known in advance, they cannot implement non-convex clustering and rapidly return to a local optimum. This study proposes the concept of DBSCAN clustering for stream sediment geochemical data. In this respect, the geochemical data collected from Varcheh district, SW Iran, were processed using the clr transformation before applying DBSCAN. Then, PCA was used to minimize the dimension of variables and specify the mineralization-related elements. In the following, one of the PCs connected with mineralization (PC2) was chosen for further analysis. DBSCAB, Mean-shift and Fuzzy K-means algorithms were used to monitor the multi-element geochemical anomalies linked to MVT PbZn deposits in the study area. According to Davies-Bouldin and Silhouette as two validation metrics, it can be deduced that the three SCB models are advantageous, however, the model generated by DBSCAN is preferable to the model generated by Mean-shift and Fuzzy K-means.

Classification Of Potential Patients Using The Fuzzy K-Means Algorithm at Woman’s and Children’s Hospital

Adina Wonosobo Woman’s and Children’s Hopsital is one of the hospitals in the Wonosobo Regency. To increase the number of patient visits, promotion is carried out through broadcast messages to patients. In evaluating its implementation, problems were found in determining the patient list of recipients of promotional broadcast messages. This problem arises because the determination of the list of recipients of promotional messages is only done on the contact numbers stored on the marketing team's mobile phones. The Fuzzy K-Means method is a data grouping method that is used as a reference for decision-making. Patient visit data were obtained from the RSIA Adina Wonosobo information system database. Furthermore, the data was analyzed to determine the potential of digital marketing priority patients using the clustering method with the Fuzzy K-Means algorithm.

Evaluasi Performa Deteksi Penyakit Diabetes Dengan Fuzzy C-Means Dan K-Means Clustering

The increasing prevalence of diabetes has led to a growing need for accurate and efficient disease detection methods. This research focuses on evaluating the performance of diabetes detection using Fuzzy C-Means and K-Means clustering algorithms. The study aims to compare the effectiveness of these two clustering techniques in identifying diabetes cases based on relevant medical data. A dataset comprising various health parameters and diagnostic indicators was utilized for experimentation. The Fuzzy C-Means and K-Means algorithms were implemented to cluster the dataset, and their detection performance was assessed using metrics such as sensitivity, specificity, accuracy, and F1-score. The results indicate that both clustering methods exhibit promising potential for diabetes detection, with variations in their performance based on different evaluation criteria. This research contributes to a deeper understanding of the applicability of clustering algorithms in diabetes detection and provides insights into their strengths and limitations. Further optimization and validation of these algorithms could lead to enhanced diagnostic accuracy and early intervention in diabetes management.

Optimization of regional economic industrial structure based on fuzzy k-means algorithm

Optimization of regional economic industrial structure based on fuzzy k-means algorithm

Diversity of Volatile Compounds in the Inula candida / I. verbascifolia Group (Asteraceae-Inuleae) and Its Impact on Species Delimitation

The members of the Inula candida / I. verbascifolia group are perennial and chasmophytic plants attributed to four species and eleven entities at subspecific or varietal level. They are mostly confined to Greece. Volatile compounds of above-ground flowering parts of twenty-three populations covering ten taxonomic entities were obtained after hydrodistillation and analyzed by gas chromatography and mass spectrometry (GC-MS). In most cases, the total percentage of identified constituents reached 89% or above. Seventy-two components were identified. Oxygenated sesquiterpenes prevailed in the I. candida subgroup (47.3–71.5%), with epi-α-cadinol present in all members (13.4–42.7%) but rarely found in the I. verbascifolia subgroup. Considerable amounts of the hydrocarbon aldehydes decanal, undecanal and particularly tridecanal (0.5–35.0%, rarely absent) were predominately found in the I. verbascifolia members but were mostly absent in the I. candida subgroup. Isoalantolactone (12.4–49.5%), identified only in I. subfloccosa, and γ-(Z)-curcumen-12-ol, found only in I. candida subsp. limonella (22.6–42.1%), may serve as chemotaxonomic markers. Two different chemotypes can be distinguished within I. verbascifolia subsp. aschersoniana: a trans-muurola-4(14),5-diene-, γ-cadinene-rich chemotype from north-eastern Greece and a 1-epi-cubenol-, tridecanal-rich chemotype from central Greece. Different statistical algorithms were used to interpret the chemical diversity and identify the most appropriate number of clusters for the taxa. Cluster analyses indicated that the optimum number of clusters that best explain the metabolomic variability of the taxa is two. The degree of membership for each population based on the fuzzy k-means algorithm supported the I. verbascifolia subsp. aschersoniana samples within the I. candida subgroup, whereas I. subfloccosa may belong to any of the two clusters formed, although it also appears to have some unique characteristics.

Hybrid Fuzzy C-Means Clustering Algorithm Oriented to Big Data Realms

A hybrid variant of the Fuzzy C-Means and K-Means algorithms is proposed to solve large datasets such as those presented in Big Data. The Fuzzy C-Means algorithm is sensitive to the initial values of the membership matrix. Therefore, a special configuration of the matrix can accelerate the convergence of the algorithm. In this sense, a new approach is proposed, which we call Hybrid OK-Means Fuzzy C-Means (HOFCM), and it optimizes the values of the membership matrix parameter. This approach consists of three steps: (a) generate a set of n solutions of an x dataset, applying a variant of the K-Means algorithm; (b) select the best solution as the basis for generating the optimized membership matrix; (c) resolve the x dataset with Fuzzy C-Means. The experimental results with four real datasets and one synthetic dataset show that HOFCM reduces the time by up to 93.94% compared to the average time of the standard Fuzzy C-Means. It is highlighted that the quality of the solution was reduced by 2.51% in the worst case.

Improving projected fuzzy K-means clustering via robust learning

Fuzzy K-Means clustering has been an attractive research area for many multimedia tasks. Due to the interference of the noise and outliers, the performance of fuzzy K-Means clustering has been limited. In this paper, a projected fuzzy K-Means clustering method, referred to as Robust Projected Fuzzy K-Means (RPFKM) is proposed as a response to such a challenge. RPFKM improves fuzzy K-Means clustering in three perspectives. First, unlike existing fuzzy K-Means algorithms where the clustering process is conducted in the original space, RPFKM learns the fuzzy membership relationship between samples and prototypes in the low-dimensional space to eliminate the influence of the noise and irrelevant features. Second, it employs the ℓ21 norm to reduce the contribution of outliers to the learning of prototypes. Third, it also considers the sensitivity of fuzzy clustering to the number of reduced dimensions, and the reconstruction term is introduced to hold the main energy of the original data. Furthermore, an iterative re-weighted algorithm is developed to solve the proposed method. The evaluation results of our proposed method and the state-of-the-art methods on real-world and synthetic data sets show the effectiveness and efficiency of our approach.

Fuzzy [formula omitted]-Means: history and applications

The fuzzy approach to clustering arises to cope with situations where objects have not a clear assignment. Unlike the hard/standard approach where each object can only belong to exactly one cluster, in a fuzzy setting, the assignment is soft; that is, each object is assigned to all clusters with certain membership degrees varying in the unit interval. The best known fuzzy clustering algorithm is the fuzzy k-means (FkM), or fuzzy c-means. It is a generalization of the classical k-means method. Starting from the FkM algorithm, and in more than 40 years, several variants have been proposed. The peculiarity of such different proposals depends on the type of data to deal with, and on the cluster shape. The aim is to show fuzzy clustering alternatives to manage different kinds of data, ranging from numeric, categorical or mixed data to more complex data structures, such as interval-valued, fuzzy-valued or functional data, together with some robust methods. Furthermore, the case of two-mode clustering is illustrated in a fuzzy setting.

COMPARATIVE STUDY OF FUZZY C-MEANS AND K-MEANS ALGORITHM FOR GROUPING CUSTOMER POTENTIAL IN BRAND LIMBACK

The customer is a stakeholder for a business, to maintain and increase customer enthusiasm and develop it for the company's performance, it is necessary to do customer segmentation which aims to find out potential customers. This study uses purchase transaction data from Brand Limback customers in the period 2020. The use of RFM (Recency, Frecuency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The datasets generated from RFM are grouped using the Fuzzy C-Means and K-Means algorithms, the two algorithms will compare the quality in the formation of clusters using the Silhoutte Coefficient and Davies-Bouldin Index methods. Customer segmentation from the RFM dataset that has been clustered produces 7 optimal clusters, namely Cluster 0 is a bronze customer. Cluster 1 is a silver customer. Cluster 2 is a gold customer. Cluster 3 is a platinum customer. Cluster 4 is a diamond customer. Cluster 5 is a super customer, and cluster 6 is a superstar customer. The cluster validation of k-means using the silhouette coefficient produces a value of 0.934 while the Davies bouldin index produces a value of 0.155 and the validation results of the fuzzy c-means algorithm using the silhouette coefficient produces a value of 0.921 while the Davies bouldin index produces a value of 0.145.

Artificial neural networks in the prediction of soil chemical attributes using apparent electrical conductivity

Aim of study: To use artificial neural networks (ANN) to predict the values and spatial distribution of soil chemical attributes from apparent soil electrical conductivity (ECa) and soil clay contents.Area of study: The study was carried out in an area of 1.2-ha cultivated with cocoa, located in the state of Bahia, Brazil.Material and methods: Data collections were performed on a sampling grid containing 120 points. Soil samples were collected to determine the attributes: clay, silt, sand, P, K+, Ca2+, Mg2+, S, pH, H+Al, SB, CTC, V, OM and P-rem. ECa was measured using the electrical resistivity method in three different periods related to soil sampling: 60 days before (60ECa), 30 days before (30ECa) and when collecting soil samples (0ECa). For the prediction of chemical and physical-chemical attributes of the soil, models based on ANN were used. As input variables, the ECa and the clay contents were used. The quality of ANN predictions was determined using different statistical indicators. Thematic maps were constructed for the attributes determined in the laboratory and those predicted by the ANNs and the values were grouped using the fuzzy k-means algorithm. The agreement between classes was performed using the kappa coefficient.Main results: Only P and K+ attributes correlated with all ANN input variables. ECa and clay contents in the soil proved to be good variables for predicting soil attributes.Research highlights: The best results in the prediction process of the P and K+ attributes were obtained with the combination of ECa and the clay content.

Apparent soil electrical conductivity in the delineation of management zones for cocoa cultivation

Apparent electrical conductivity is an important parameter for describing the spatial variability of physical and chemical attributes of the soil and for the delineation of management zones. The objective of this work is to outline management zones for the cocoa cultivation based on the spatial variability of the productivity and the apparent electrical conductivity (ECa) of the soil. Data collection was performed in a regular sample grid containing 120 points in an area cultivated with cocoa trees, located in the municipality of Ilhéus, state of Bahia, Brazil. At each sampling point (cocoa tree), soil samples were collected to determine chemical attributes. Productivity was measured for one year, counting, monthly, the number of fruits, which were classified into off-season cocoa, harvest and annual production. Measurements of the apparent electrical conductivity of the soil were performed at different times of the year using a portable conductivity meter. The data were analyzed using classical statistics and geostatistics. The management zones were delineated using the fuzzy k-means algorithm. The ideal number of class was defined using the fuzziness performance index (FPI) and the entropy of the modified partition (MPE) indexes. The Kappa coefficient was used to validate the management zones, assessing their agreement with the chemical attributes of the soil. The ECa of the soil values presented moderate temporal variation, with maximum amplitude of 19.37mSm−1 and minimum of 0.82mSm−1 between measurement periods; higher averages of the ECa coincided with the highest levels of water in the soil. The measurements of the ECa of the soil carried out in April and October showed greater correlation with the chemical attributes of the soil, with significant values for 11 and 8 of the 17 attributes evaluated, respectively. The management zones from the ECa measured in April showed: a) reduced number of classes; b) spatial continuity between classes, and; c) agreement from reasonable (kappa between 0.20 and 0.40) to good (kappa > 0.41) with most of the chemical attributes of the soil. The ECa of the soil measured in April is, individually, the variable recommended for the management of soil fertility in tropical areas cultivated with cocoa trees.

Spatiotemporal Variation in Ecosystem Services and Their Drivers among Different Landscape Heterogeneity Units and Terrain Gradients in the Southern Hill and Mountain Belt, China

Understanding the spatiotemporal heterogeneity of ecosystem services (ESs) and their drivers in mountainous areas is important for sustainable ecosystem management. However, the effective construction of landscape heterogeneous units (LHUs) to reflect the spatial characteristics of ESs remains to be studied. The southern hill and mountain belt (SHMB) is a typical mountainous region in China, with undulating terrain and obvious spatial heterogeneity of ESs, and was selected as the study area. In this study, we used the fuzzy k-means (FKM) algorithm to establish LHUs. Three major ESs (water yield, net primary productivity (NPP), and soil conservation) in 2000 and 2015 were quantified using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and Carnegie Ames-Stanford approach (CASA) model. Then, we explored the spatial variation in ESs along terrain gradients and LHUs. Correlation analysis was used to analyze the driving factors of ESs in each terrain region and LHU. The results showed that altitude and terrain niche increased along LHUs. Water yield and soil conservation increased from 696.86 mm and 3920.19 t/km2 to 1061.12 mm and 5117.90 t/km2, respectively, while NPP decreased from 666.95 gC/m2 to 648.86 gC/m2. The ESs in different LHUs differed greatly. ESs increased first and then decreased along LHUs in 2000. In 2015, water yield decreased along LHUs, while NPP and soil conservation showed a fluctuating trend. Water yield was mainly affected by precipitation, temperature and NDVI were the main drivers of NPP, and soil conservation was greatly affected by precipitation and slope. The driving factors of the same ES were different in different terrain areas and LHUs. The variation and driving factors of ESs in LHUs were similar to some terrain gradients. To some extent, LHUs can represent multiple terrain features. This study can provide important support for mountain ecosystem zoning management and decision-making.

A Framework of Mutual Information Kullback-Leibler Divergence based for Clustering Categorical Data

Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means Algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the proposed method's comparison results with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed Algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.

Ensemble clustering using extended fuzzy k-means for cancer data analysis

Clustering analysis is a significant research topic in discovering cancer using different profiles of gene expression, which is very important to successfully diagnose and treat the cancer decease. Many ensemble clustering methods have been developed to perform clustering using tumor data. Only few of them incorporates a significant number of input clusterings, the optimal number of clusters in each input clustering, and an appropriate ensemble method to combine input clusterings into a final clustering. In this paper, we introduce two new steps in the standard fuzzy k-means algorithm to determine the optimal number of input clusterings, and the optimal number of clusters in each clustering for ensemble clustering. The first one is to incorporate a penalty term for making the algorithm insensitive to the initialization of cluster centroids. The second one is to automate a clustering process for iteratively updating the feature weights. This step addresses the noise values in the dataset. We propose an ensemble clustering method, which combines a set of input clusterings into a final clustering having better overall quality. Experiments on real cancer gene expression profiles illustrate that the proposed algorithm outperformed the well-known clustering algorithms.

A Unified Form of Fuzzy C-Means and K-Means algorithms and its Partitional Implementation

This paper proposes as an element of novelty the Unified Form (UF) clustering algorithm, which treats Fuzzy C-Means (FCM) and K-Means (KM) algorithms as a single configurable algorithm. UF algorithm was designed to facilitate the FCM and KM algorithms software implementation by offering a solution to implement a single algorithm, which can be configured to work as FCM or KM. The second element of novelty of this paper is the Partitional Implementation of Unified Form (PIUF) algorithm, which is built upon the UF algorithm and designed to solve in an elegant manner the challenges of processing large datasets in a sequential manner and the scalability of the UF algorithm for processing datasets of any size. PIUF algorithm has the advantage of overcoming any possible hardware limitations that can occur if large volumes of data are processed (required to be stored, loaded in memory and processed by a certain specified computational system). PIUF algorithm is designed and formulated to be used on a single machine if the processed dataset is very big and it cannot be entirely loaded in the memory; at the same time it can be scaled to multiple processing nodes for reducing the processing time required to find the optimal solution. UF and PIUF algorithms are implemented and validated in BigTim platform, which is a distributed platform developed by the authors, and offers support for processing various datasets in a parallel manner but they can be implemented in any other data processing platforms. The Iris dataset is considered and next modified to obtain different datasets of different sizes in order to test the algorithms implementations in BigTim platform in different configurations. The analysis of PIUF algorithm and the comparison with FCM, KM and DBSCAN clustering algorithms are carried out using two performance indices; three performance indices are employed to evaluate the quality of the obtained clusters.

High-Speed and Accurate Meat Composition Imaging by Mechanically-Flexible Electrical Impedance Tomography With k-Nearest Neighbor and Fuzzy k-Means Machine Learning Approaches

High-speed and accurate meat composition imaging method has been proposed based on mechanically-flexible electrical impedance tomography (mech-f-EIT) with k-nearest neighbor and fuzzy k-means machine learning approaches. This proposed method has four stages which are 1) estimation of meat boundary shape ∂Ω by mech-f-EIT for base data, 2) approximation of Jacobian matrix J* by k-nearest neighbor (k-NN) algorithm under ∂Ω for high speed, 3) clustering of meat composition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> σ (fat k = 1, lean k = 2, bone k = 3) by fuzzy k-means algorithm based on the reconstructed meat conductivity distribution σ for high accuracy, and 4) edge detection of meat composition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> Ω by Canny algorithm for sharp edge. This method is qualitatively evaluated by using two agar phantoms, a cow's lower leg and three lamb's lower legs. As the results, mech-f-EIT estimates ∂Ω with total mean boundary error 〈ẽ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> 〉 = 4.81 %. This method achieves high-speed approximation of J* with total mean speed-up performance 〈s̃p〉 = 4.51 times as compared with the computation time of standard J; nonetheless, total mean cross correlation between J* and J is accurate 〈c̃ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> 〉 = 0.92. Moreover, this method clusters the <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> σ with total mean area error 〈ẽ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> 〉 = 4.49 %. Furthermore, this imaging method detects sharply the meat composition edges <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> Ω between fat and lean (k = 1 - 2) and between lean and bone (k = 2 - 3) with total mean edge error 〈ẽ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> 〉 =6.90 %.

Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

Treating malignant growth in the beginning times gives greater treatment choices, less intrusive medical procedure and expands the endurance rate. Finding lung disease just as liver malignancy at a beginning period is a difficult assignment since there are hardly any manifestations right now larger part of the cases are analyzed in later stages. In this paper, an augmented method of segmentation is proposed using the Fuzzy K-means algorithm to observe the initial stage of lung cancer from the human chest X-Ray images. Initially, the investigator used strategies like Histogram Equalization for image improvement, Watershed technique for segmentation and Edge detection for Extraction, etc. Existing Approaches decline to provide certainty in a problem-solving time operation. The datasets are collected from LIDC-IDRI and Kaggle. In this, 100 training dataset input images are taken for training the model: 80 Cancerous images and 20 non-cancerous images. To evaluate the data 20 images are taken. The detection means classifying tumour into three classes (i) non-cancerous tumour, (ii) Less affected tumour (iii) Highly affected tumour. Hence, a new technique to detect lung carcinoma nodules using median filters, Fuzzy K-means clustering for segmentation and SVM (Support Vector Machine) as the classifier is planned on this work. Hence, this way is profitable to the medical instruments producing industries and additionally guide the radiologist for the early analysis of cancer.

RETRACTED ARTICLE: Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images

The primary objective of this paper is to develop a methodology for brain tumor segmentation. Nowadays, brain tumor recognition and fragmentation is one among the pivotal procedure in surgical and medication planning arrangements. It is difficult to segment the tumor area from MRI images due to inaccessibility of edge and appropriately visible boundaries. In this paper, a combination of Artificial Neural Network and Fuzzy K-means algorithm has been presented to segment the tumor locale. It contains four phases, (1) Noise evacuation (2) Attribute extraction and selection (3) Classification and (4) Segmentation. Initially, the procured image is denoised utilizing wiener filter, and then the significant GLCM attributes are extricated from the images. Then Deep Learning based classification has been performed to classify the abnormal images from the normal images. Finally, it is processed through the Fuzzy K-Means algorithm to segment the tumor region separately. This proposed segmentation approach has been verified on BRATS dataset and produces the accuracy of 94%, sensitivity of 98% specificity of 99%, Jaccard index of 96%. The overall accuracy of this proposed technique has been improved by 8% when compared with K-Nearest Neighbor methodology.