Gradient based fuzzy c-means (GBFCM) algorithm

This work presents a detailed framework to detect the location of heart sound within the respiratory sound based on temporal fuzzy c-means (TFCM) algorithm. In the proposed method, respiratory sound is first divided into frames and for each frame, the logarithmic energy features are calculated. Then, these features are used to classify the respiratory sound as heart sound (HS containing lung sound) and non-HS (only lung sound) by the TFCM algorithm. The TFCM is the modified version fuzzy c-means (FCM) algorithm. While the FCM algorithm uses only the local information about the current frame, the TFCM algorithm uses the temporal information from both the current and the neighboring frames in decision making. To measure the detection performance of the proposed method, several experiments have been conducted on a database of 24 healthy subjects. The experimental results show that the average false-negative rate values are 0.8 ± 1.1 and 1.5 ± 1.4 %, and the normalized area under detection error curves are 0.0145 and 0.0269 for the TFCM method in the low and medium respiratory flow rates, respectively. These average values are significantly lower than those obtained by FCM algorithm and by the other compared methods in the literature, which demonstrates the efficiency of the proposed TFCM algorithm. On the other hand, the average elapsed time of the TFCM for a data with length of 0.2 ± 0.05 s is 0.2 ± 0.05 s, which is slightly higher than that of the FCM and lower than those of the other compared methods.

Fuzzy C-Means (FCM) algorithm is a fuzzy pattern recognition method. Clustering precision of the algorithm is affected by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian function Weighted Fuzzy C-Means (WFCM) algorithm is proposed, which the weighted function is produced by a Gaussian function calculating dot density of each sample. To certain extent, the WFCM algorithm has not only overcome the limitation of equal partition trend in fuzzy Cmeans algorithm, but also been favorable convergence and stability. The calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFCM algorithm, the classification performance of the WFCM algorithm is further enhanced and the convergent speed of objective function is further accelerated.

This paper shows that a choice of weights in the fuzzy c-means algorithm affect the performance of a room equalizer. The ordinary fuzzy c-means (FCM) algorithm already proves useful to deriving an equalizing filter for multiple listeners. A room impulse response models the behaviour of the sounds reflection in the room at each listener location. The FCM algorithm finds similar room impulse responses based on Euclidean distances among the impulse responses and groups them together. But the Euclidean distance used in the FCM implies equal weights for all parts of sound reverberation or reflection. The paper proposes an algorithm to obtain a room acoustic equalization filter based on weighted fuzzy c-means (WFCM) algorithm. The use of WFCM can give clustering more flexibility in obtaining a prototype for each cluster. Then combining the prototypes to obtain a representative room response that derives the inverse filter will be more effective. We use spectral deviation to measure how well each equalizing filter works. Experiments show that different weights in the WFCM algorithm can enhance or degrade the performance of the equalizing filter.

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal magnetic resonance (MR) images. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. The effectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, the application of modified FCM algorithm for MR brain tumor detection is explored. Abnormal brain images from four tumor classes namely metastase, meningioma, glioma and astrocytoma are used in this work. A comprehensive feature vector space is used for the segmentation technique. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures.

Bias-correction fuzzy clustering algorithms

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-means (FCM) algorithm is most frequently used for fuzzy clustering. But some fixed points of FCM algorithm, known as Tucker's counter example, is not a reasonable solution. Moreover, the FCM algorithm is impossible to perform online learning since it is basically a batch learning scheme. This paper presents an unsupervised learning network as an attempt to improve the shortcomings of conventional clustering algorithms. This model integrates the optimization function of FCM algorithm into an unsupervised learning network. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of fuzzy objective function. Using the result of formal derivation, two implementations of the proposed scheme, the batch learning version and online learning version, are devised. They are tested on Chiou's data and Iris data and compared with FCM. Experimental results show that the proposed scheme derived the reasonable solution on Tucker's counter example.

In this paper, we show how one can take advantage of the stability and effectiveness of object data clustering algorithms when the data to be clustered are available in the form of mutual numerical relationships between pairs of objects. More precisely, we propose a new fuzzy relational algorithm, based on the popular fuzzy C-means (FCM) algorithm, which does not require any particular restriction on the relation matrix. We describe the application of the algorithm to four real and four synthetic data sets, and show that our algorithm performs better than well-known fuzzy relational clustering algorithms on all these sets.

In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.

The control of pH process has a vast range of applications in wastewater treatment, biochemical and electrochemical processes, the paper and pulp industry and many other areas. Tight control of pH is also critical in the production of pharmaceuticals. However, the dynamics of pH process is highly nonlinear, time varying with change in gain of several orders. It is very difficult to investigate the dynamic behavior of such systems using conventional modeling techniques thereby designing controller parameters. In this paper, pole placement based PI controller is designed for a experimental pH process, Takagi-Sugeno (T-S) model is developed for a pH process using fuzzy c-means (FCM) algorithm and particle swarm optimization (PSO) based FCM algorithm. The performance of the proposed model FCM with PSO is compared with the results obtained by fuzzy c-means algorithm. The comparison shows the superiority of the proposed model. The proposed model can be used to develop model based control techniques.

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

The Fuzzy C-Means (FCM) algorithm is commonly used for clustering. The weighting exponent c and the clustering number m are the important parameters in FCM algorithm. Conventional fuzzy clustering method must use both of the two prespecified parameters, so in this paper we analyses the original algorithm and studies on the optimal selection methods of the c and m by introducing the fuzzy decision theory and the validity index basing on the geometric structure of the dataset. Experiment results with the IRIS dataset show that this algorithm can obtain the optimal weighting exponent m and the optimal clustering number c. Moreover, the fact that the best value scope of m achieved in practical applications indicates that the method is effective.

Turkey has seven traditionally accepted climatic zones that are defined primarily by maritime and topographic influences. Across these zones, the annual amount of rainfall, including its intensity and its seasonal distribution, vary considerably. These variations, which impact on both urban and rural communities, including the occurrence of water shortages and flash flooding events, are increasing in both frequency and magnitude due to global warming and climate change. Several types of climate occur in Turkey where climate zones have been defined with various methodologies. To better understand rainfall intensity patterns across Turkey, this study used the Fuzzy C-Means (FCM) algorithm to define their spatial distribution. In the first stage, the annual maximum rainfall intensity records for periods ranging from 30 to 78 years were obtained from 95 stations operated by the Turkish State Meteorological Service, and the longitude, latitude and altitude data for the stations were compiled for cluster analysis. Secondly, all rainfall intensities and geographical values were normalized, and in the third stage, the FCM algorithm was applied. The comparison of annual maximum rainfall intensities revealed five clusters. Four clusters were identified as discrete zones and one was identified as a transitional zone. Weather stations located in different geographical regions sometimes fell into the same clusters. In other words, rainfall events of similar intensity can occur in different climatic zones. This study, which brought a different perspective to clustering studies, showed that rainfall intensity values can be successfully analyzed at a national scale with the FCM technique.

Fuzzy C-Means (FCM) algorithm has good clustering efficiency, for which it is widely used in the field of image segmentation. However, problems such as weak robustness of distance measure, the number of initial clustering to be given in advance and not considering local image feature still exist. In essence, FCM is a local search algorithm. Improper selection of initial value will lead to the need for more iterations and convergence to local optimal solution. By combining evolving clustering (ECM) with FCM algorithm, a new method of remote sensing image segmentation is put forward. By using FCM algorithm to solve the choice of ECM's initialization clustering centers and using FCM to optimize the obtained centers, fuzzy clustering is completed. And by converting fuzzy into a certainty classification, clustering segmentation is realized. The algorithm is typical of relatively less iterations of convergence to global optimum, good stability and robustness. Experiment results show that it helps to produce better segmentation effect and improve the efficiency of remote sensing image segmentation.

In order to overcome the poor anti-noise performance of traditional fuzzy C-Means (FCM) algorithm in image segmentation, a novel improved FCM algorithm was proposed in this paper based on Particle Swarm Optimization (PSO) algorithm and Markov Random Field (MRF) model, which can make full use of the global searching ability of PSO and the spatial information integrating ability of MRF for image segmentation. In this algorithm, the image segmentation is converted to a PSO optimization problem, in which the fitness function is set up to containing the spatial information based on the spectral value and the neighboring pixels modeled by MRFs. And segmentation results can be iteratively obtained during the PSO iterations according to the newly designed membership function of FCM in which the spatial information is integrated. The experiments herein reported in this paper illustrate the better performance of this algorithm than the traditional FCM algorithm and the PSO algorithm for image segmentation.