Improved Fuzzy C-Means Research Articles

A Fuzzy C-Means Clustering Algorithm Based on Spatial Context Model for Image Segmentation

An improved Fuzzy C-Means (FCM) algorithm, which is called Reliability-based Spatial context Fuzzy C-Means (RSFCM), is proposed for image segmentation in this paper. Aiming to improve the robustness and accuracy of the clustering algorithm, RSFCM integrates neighborhood correlation model with the reliability measurement to describe the spatial relationship of the target. It can make up for the shortcomings of the known FCM algorithm which is sensitive to noise. Furthermore, RSFCM algorithm preserves details of the image by balancing the insensitivity of noise and the reduction of edge blur using a new fuzzy measure indicator. Experimental data consisting of a synthetic image, a brain Magnetic Resonance (MR) image, a remote sensing image, and a traffic sign image are used to test the algorithm’s performance. Compared with the traditional fuzzy C-means algorithm, RSFCM algorithm can effectively reduce noise interference, and has better robustness. In comparison with state-of-the-art fuzzy C-means algorithm, RSFCM algorithm could improve pixel separability, suppress heterogeneity of intra-class objects effectively, and it is more suitable for image segmentation.

Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k-means (KM) and fuzzy c-means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k-means (IKM) and improved fuzzy c-means (IFCM) clustering algorithms segment the INSAT-3D satellite's thermal infrared image into low-level, middle-level, high-level clouds and non-cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non-cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms.

A Novel Hybrid Active Contour Model for Intracranial Tuberculosis MRI Segmentation Applications

This paper proposes an improved region-based active contour model for segmenting magnetic resonance imaging (MRI) images of brain tuberculosis by combining a global energy fitting term and a local energy fitting term. First, a global energy fitting term is utilized to extract global image information, which guides the evolving curve globally and approximates the image intensity inside and outside the contour. Second, a local energy fitting term is proposed to describe the intensity inhomogeneity based on the local intensity variance and the adaptive image difference. Third, an improved Fuzzy C-Means (FCM) clustering method is applied to pre-segment the MRI images to automatically track the approximate location of brain tuberculosis and provide the initial contour for the hybrid model segmentation. By combining the global and local weighting functions, a hybrid region-based model is defined. Experiments demonstrate that the proposed model provides initialization of the contours automatically and offers superior segmentation performance for brain tuberculosis MRI medical images with intensity inhomogeneity.

A New Hybrid Method for Effective Computer Aided Diagnosis to Predict Breast Tumor in Elderly Patients

Breast cancer related morbidity has surged in recent years due to lack of awareness and failure to have an early prediction. With technological advancements available, doctors have put-forth their fullest effort to downsize the death-rate. Despite, certain areas of patient diagnosis, specifically with the case of breast cancers need a symbiotic improvement, and should greatly help the patients to lead a better and healthy life. For such extents, we authors like to propose a new hybrid approach that encompasses the functioning of Particle Swarm Optimization (PSO) and Improved Fuzzy C-Means (IFCM) through this paper. The technique recommended has tendered an elevation in the values of PSNR, TC and DOI, which most of the contemporary algorithms/techniques have failed to achieve, thus assisting the Doctors to have a profound idea regarding the prevalence of breast cancer and tumor.

An Automated Map Process Based Improved Fuzzy C-Means Algorithm for Pathological Detection in MR Image

Automated brain MR slices segmentation process is difficult, and further difficult is the process of detecting the tumor and tissue regions, with a constraint of delivering higher segmentation accuracy within reduced processing time. Automated algorithms were developed with an onus of reducing the intricacies involved during the manual inspection of the pathologies (radiologist/operator involvement). The shortages of an automated process are overthrown with the development of a novel combination of soft computing algorithms, and it employs automated map and clustering approaches. Self-Organizing map (SOM) and Improved Fuzzy C-Means clustering (IFCM) are the automated map and clustering approaches that are used to precisely provide the MRI slice analysis. The authors have utilized the quality metrics, such as Dice overlap Index (DOI), Jaccard index, Peak Signal to Nosie Ratio (PSNR) and Mean Squared Error (MSE) for verifying the performance of the SOM based IFCM, and the recommended algorithm tenders the corresponding values of the above as 84.83%, 91.69%, 0.0824 and 49.25dB. The novel SOM- IFCM algorithm delivers better demarcation outcomes when compared with other soft computing approaches. The exemplified outcomes of the proposed SOMIFCM algorithm provides superior segmentation quality of MR brain slices and offers versatile usage to the radiologists

An adaptive clustering and classification algorithm for Twitter data streaming in Apache Spark

On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy. Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which can cluster data within a lesser computational time, especially for data streaming is needed. The presented adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization. At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of presented approach comparing with the existing methods in terms of precision, recall, F-score, convergence, ROC curve and accuracy.

Optical classification of inland waters based on an improved Fuzzy C-Means method.

Water optical clustering based on water color information is important for many ecological and environmental application studies, both regionally and globally. The fuzzy clustering method avoids the sharp boundaries in type-memberships produced by hard clustering methods, and thus presents its advantages. However, to make good use of the fuzzy clustering methods on water color spectra data sets, the determination of the fuzzifier parameter (m) of FCM (fuzzy c-means) is the key factor. Usually, the m is set to 2 by default. Unfortunately, this method assigned some membership degrees to non-belonging water type, failing to obtain the unitarity of cluster structure in some cases, especially in inland eutrophic water. To overcome this shortcoming, we proposed an improved FCM method (namely FCM-m) for water color spectra classification by optimizing the fuzzifier parameter. We collected an inland data set containing 1280 in situ spectral data and co-measured water quality parameters with a wide range of biogeochemical variability in China. Using FCM-m, seven spectrally distinct water optical clusters on Sentinel-3 OLCI (Ocean and Land Colour Imager) bands were obtained with the optimized fuzzifier (m=1.36), and the well-performed clustering result is assessed by the validated index (Fuzzy Silhouette Index=0.513). Also, the FCM-m-based soft classification framework was successfully applied to the atmospherically corrected OLCI images, which was evaluated by previous case studies. Besides, by testing FCM-m on three coastal and oceanic data sets, we verified that the optimized m should be adjusted based on the data set itself, and in general, the value gradually approaches 1 with the increase of the band number (or dimension). Finally, the effect of the improved method was tested by Chlorophyll-a concentration estimation. The results show that the algorithm------- blending by FCM-m performs better than that by original FCM, which is mainly because the FCM-m reduces the estimation error from non-belonging clusters by a stricter membership value assignation. To sum up, we believe that FCM-m is an adaptive algorithm, whose R codes are available at https://github.com/bishun945, and needs to be tested by more public data sets.

Edge Detection of Images Using Improved Fuzzy C-Means and Artificial Neural Network Technique

Edge detection (ED) is an embryonic development, which is essential for any intricate image processing and recognition undertaking. This paper proposed another system to upgrade the method and Artificial neural network for speaking to vulnerability in the image slopes and collection. The vulnerability in the image inclination distinguishes the genuine edges which might be overlooked by other systems. This e is valuable in the field of restorative imaging applications, for example, MRI division, cerebrum tumor, filtering and so on. Attractive reaction imaging connected in restorative science to analyze tumors in body parts by creating great images of within the human body, by utilizing different edge identifiers. There exist many edge finders yet at the same time, requirement for inquire about is felt improve their execution. And furthermore, this paper distinguishes the edges in the broken bones, edge ID, satellite edge detection ID. An exceptionally basic issue looked by many edge finders is the decision of limit esteems. This paper presents fuzzy and ANN based edge detection utilizing Improved Fuzzy C-means clustering (FCM) strategy. Enhanced FCM approach is utilized in producing different gatherings which are then contribution to the Mamdani fuzzy surmising framework. In this, we are utilizing versatile middle separating for evacuating commotion; this strategy adequately expels the clamor and gives better outcomes. This entire procedure results in the age of the limit parameters which is then encouraged to the established sobel edge locator which helps in improving its edge detection capacity utilizing the fuzzy logic. This entire setup is connected to Images. The recovered outcomes express to that fuzzy and ANN based Improved Fuzzy C-means clustering enhances the introduction of customary sobel edge identifier in associate with retentive information around the tumors of the mind.

Indoor Localization Method Based on Regional Division with IFCM

With the development of wireless technology, indoor localization has gained wide attention. The fingerprint localization method is proposed in this paper, which is divided into two phases: offline training and online positioning. In offline training phase, the Improved Fuzzy C-means (IFCM) algorithm is proposed for regional division. The Between-Within Proportion (BWP) index is selected to divide fingerprint database, which can ensure the result of regional division consistent with the building plane structure. Moreover, the Agglomerative Nesting (AGNES) algorithm is applied to accomplish Access Point (AP) optimization. In the online positioning phase, sub-region selection is performed by nearest neighbor algorithm, then the Weighted K-nearest Neighbor (WKNN) algorithm based on Pearson Correlation Coefficient (PCC) is utilized to locate the target point. After the evaluation on the effect of regional division and AP optimization of location precision and time, the experiments show that the average positioning error is 2.53 m and the average computation time of the localization algorithm based on PCC reduced by 94.13%.

An improved partitioning algorithm based on FCM algorithm for extended target tracking in PHD filter

Extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter is a promising approach for multi-extended target tracking problem. Correct partitions of measurements are significant to the GM-PHD filter. In order to partition the measurement sets accurately and rapidly, an improved Fuzzy C-Means (FCM) approach is proposed. The proposed approach consists of two stages. Firstly, elliptical gating and Gaussian kernel density analysis technique are used to remove the clutter. Secondly, an improved FCM approach which takes the full merits of the prior information of targets is implemented. The predictive state of targets is utilized in the FCM initialization. And the shape of targets is considered in the iteration of FCM. Both real data and simulated data are performed in this work. Experimentation infers that the proposed method outperforms the others on both effectiveness and computation, especially when multiple closely targets are involved.

Information geometry enhanced fuzzy deep belief networks for sentiment classification

With the development of internet, more and more people share reviews. Efficient sentiment analysis over such reviews using deep learning techniques has become an emerging research topic, which has attracted more and more attention from the natural language processing community. However, improving performance of a deep neural network remains an open question. In this paper, we propose a sophisticated algorithm based on deep learning, fuzzy clustering and information geometry. In particular, the distribution of training samples is treated as prior knowledge and is encoded in fuzzy deep belief networks using an improved Fuzzy C-Means (FCM) clustering algorithm. We adopt information geometry to construct geodesic distance between the distributions over features for classification, improving the FCM. Based on the clustering results, we then embed the fuzzy rules learned by FCM into fuzzy deep belief networks in order to improve their performance. Finally, we evaluate our proposal using empirical data sets that are dedicated for sentiment classification. The results show that our algorithm brings out significant improvement over existing methods.

Symmetry Information Based Fuzzy Clustering for Infrared Pedestrian Segmentation

Pedestrian detection in infrared images is always a challenging task. Segmentation is an important step of pedestrian detection. An accurate segmentation could provide more information for further analysis. In this paper, an improved Fuzzy C-Means clustering method, which incorporates geometric symmetry information, is proposed for infrared pedestrian segmentation. In the proposed method, symmetry information is introduced by Markov random field theory. Moreover, a new metric is utilized to handle the weak symmetry of pedestrian. In addition, a whole procedure is proposed to extract infrared pedestrians. The experimental results indicate that our method performs better for infrared pedestrian segmentation and obtains better segmentation results compared with other state-of-the-art methods.

An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization

The fuzzy c-means (FCM) algorithm is the most popular clustering method. Many studies of FCM had been done. However, the FCM algorithm and its studies are usually affected by the selection of initial values and noise data, and can easily fall into local optimal value. To overcome these drawbacks of FCM, this paper proposed the algorithm of FCM based on multi-chain quantum bee colony algorithm (MQBC-FCM). In MQBC-FCM, first, the multiple chains encoding method is introduced to the artificial bee colony algorithm to propose the MQBC algorithm. Then MQBC is used to search for the optimal initial clustering centers. The proposed algorithm is used on artificial data sets and image segmentations, and its performance is contrasted with several algorithms. The experimental results have indicated that the proposed MQBC-FCM has efficiently improved the performance of the clustering algorithm.

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.

An Improved Fuzzy C-Means Clustering Algorithm Based on Potential Field

In the area of recommendation system, clustering is an effective way to find similar users and reduce the complexity of recommendation algorithm. A good clustering result has two characteristics: maximizing compactness within cluster and maximizing separation between clusters. Following laws of attraction and repulsion, electric charges in an electric potential field have the same feature as clusters. Based on potential field, an improved Fuzzy C-Means (FCM) clustering algorithm is proposed, which is called EFCM (Electrical FCM). This algorithm combines the basic electrical rules of potential field, Coulomb's law and data field theory to obtain better clustering results. The experiment results show that the improved EFCM algorithm not only obtains good initial cluster centers, but also helps to improve the process of iterative updating.

A meteorological information mining-based wind speed model for adequacy assessment of power systems with wind power

Accurate wind speed simulation is an essential prerequisite to analyze the power systems with wind power. A wind speed model considering meteorological conditions and seasonal variations is proposed in this paper. Firstly, using the path analysis method, the influence weights of meteorological factors are calculated. Secondly, the meteorological data are classified into several states using an improved Fuzzy C-means (FCM) algorithm. Then the Markov chain is used to model the chronological characteristics of meteorological states and wind speed. The proposed model was proved to be more accurate in capturing the characteristics of probability distribution, auto-correlation and seasonal variations of wind speed compared with the traditional Markov chain Monte Carlo (MCMC) and autoregressive moving average (ARMA) model. Furthermore, the proposed model was applied to adequacy assessment of generation systems with wind power. The assessment results of the modified IEEE-RTS79 and IEEE-RTS96 demonstrated the effectiveness and accuracy of the proposed model.

Fuzzy C-means Clustering with Temporal-based Membership Function

Objective: In this paper, a method is proposed to create clusters depending on temporal information. Despite its popularity, the FCM algorithm does not utilize temporal information in creating clusters, hence affecting the accuracy of clustering. This paper presents an improved Fuzzy C-means algorithm that incorporates temporal information into the membership function used for clustering. Methods: The proposed FCM algorithm employs temporal neighbouring of data points as the base of clustering. In order to evaluate the algorithm, experimental analysis was performed on three multi-labelled datasets, including a clinical free text (medical), textual email messages (Enron), and Bibtex. Finding: The experimental results show that the proposed function contributes a smaller value of objective function while using a minimum number of iterations. Application: The proposed work will benefit data mining in various domains such as information retrieval, healthcare, business management and many others. This is due to its ability in grouping data- points that are not mutually exclusive.

Arrhythmia classification using Mahalanobis distance based improved Fuzzy C-Means clustering for mobile health monitoring systems

In this paper, an improved electrocardiogram (ECG) beats classification system is proposed, which is based on Fuzzy C-Means (FCM) clustering algorithm. The classification of ECG beats is necessary in order to diagnose the type of arrhythmia (e.g., Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), Right Bundle Branch Block (RBBB) etc.) present in the ECG records. The efficiency of any classification model highly depends on the “most relevant” set of features used. The primary goal of this study is to classify different arrhythmic beats with reduced set of relevant-only ECG attributes. The attribute selection model is based on Mahalanobis-Taguchi System (MTS); a multi-dimensional pattern recognition tool, which can dynamically choose the important set of ECG features. The number of most relevant features can vary from person to person according to the type of arrhythmia present in the respective ECG signals. A traditional Euclidian Distance (ED) based FCM can detect the spherical clusters but it may lead to improper clustering in some cases. As a solution to this problem, Mahalanobis Distance (MD) is used in the proposed model in order to improve the distance measurement procedure. In our proposed system, MD based improved Fuzzy C-Means (FCM-M) clustering is used to classify the arrhythmic beats. Experimental results show that the performance of FCM-M is significantly better than the conventional FCM for arrhythmia classification. Another direction of our proposed research is to use the concept of initial cluster centroid in order to reduce the number of program iterations. In our experiments, the number of program iterations is reduced to an average of 53% when initial centroid is assigned to FCM-M with the same classification results.

Unsupervised Binning of Metagenomic Assembled Contigs Using Improved Fuzzy C-Means Method

Metagenomic contigs binning is a necessary step of metagenome analysis. After assembly, the number of contigs belonging to different genomes is usually unequal. So a metagenomic contigs dataset is a kind of imbalanced dataset and traditional fuzzy c-means method (FCM) fails to handle it very well. In this paper, we will introduce an improved version of fuzzy c-means method (IFCM) into metagenomic contigs binning. First, tetranucleotide frequencies are calculated for every contig. Second, the number of bins is roughly estimated by the distribution of genome lengths of a complete set of non-draft sequenced microbial genomes from NCBI. Then, IFCM is used to cluster DNA contigs with the estimated result. Finally, a clustering validity function is utilized to determine the binning result. We tested this method on a synthetic and two real datasets and experimental results have showed the effectiveness of this method compared with other tools.

Improved Fuzzy C-Mean Algorithm for Image Segmentation

The segmentation of image is considered as a significant level in image processing system, in order to increase image processing system speed, so each stage in it must be speed reasonably. Fuzzy c-mean clustering is an iterative algorithm to find final groups of large data set such as image so that is will take more time to implementation. This paper produces an improved fuzzy c-mean algorithm that takes less time in find cluster and used in image segmentation.