Fuzzy C Means

A fuzzy clustering-based hybrid method for a multi-facility location problem is presented in this study. It is assumed that capacity of each facility is unlimited. The method uses different approaches sequentially. Initially, customers are grouped by spherical and elliptical fuzzy cluster analysis methods in respect to their geographical locations. Different numbers of clusters are experimented. Then facilities are located at the proposed cluster centers. Finally each cluster is solved as a single facility location problem. The center of gravity method, which optimizes transportation costs is employed to fine tune the facility location. In order to compare logistical performance of the method, a real world data is gathered. Results of existing and proposed locations are reported.

Image segmentation is a growing field and it has been successfully applied in various fields such as medical imaging, face recognition, etc. In this paper, we propose a method for image segmentation that combines a region based artificial intelligence technique named fuzzy c-means (FCM) and a boundary based mathematical modeling technique level set method (LSM). In the proposed method, the contour of the image is obtained by FCM method which serves as initial contour for LSM Method. The final segmentation is achieved using LSM which uses signed pressure force (spf) function for active control of contour.

Stock market data is a high dimensional time series financial data that poses unique computational challenges. Stock data is variable in terms of time, predicting the future trend of the prices is a challenging task. The factors that influence the predictability of stock data cannot be judged as the same factors may or may not influence the value of the stock all the time. We propose a data mining approach for the prediction of the movement of stock market. It includes using the genetic algorithm for pre processing and a hybrid clustering approach of Hierarchical clustering and Fuzzy C-Means for clustering. The genetic algorithm helps in dimensionality reduction and clustering helps to create feature vectors that help in prediction.

In this paper we show apply text mining techniques, Correspondence Analysis and Fuzzy C-Means Clustering in order to identify associations among countries and titles of documents available at a profile in Academia.edu. All analysis was carried out by R software and several R scripts are presented.

Tourism is without a doubt one of the most important forces shaping our world. According to the latest statistics of WTO (2008) the industry shows a constantly increasing participation in production worldwide. The number of international arrivals has increased almost 34 times since the 50's. Besides its economic contribution, tourism can help in the promotion of peace and understanding between peoples. Tourism is essentially a geographic activity. Tourism is also a multi-faced activity and a geographically complex one as different services are sought supplied and consumed at different stages from the origin to the destination. Moreover in any country or region there is likely to be a number of origins and destinations, with most places having both generating (origin) and receiving (destination) functions (Pearce, 1987a). The above complexity of tourism development according to spatial and temporal stages means that policies should also vary. Tourism policy should be specific for each and every case (destination area) in order to be effective and the goal of sustainable development to be achieved. But in the other hand it is almost impossible to form a policy for each case in a national or regional level or even more in a global scale. In this case supply typologies for tourism can help in the decision making process and to the formulation of policies according to each type of areas. Main scope of this paper is to show a new way of creating a supply typology for tourism for policy making reasons together with the use of advanced statistical methods and new technologies. The case study area of this paper is the Greek coastline (mainland and islands). Geographical Information Systems and Fuzzy c Means were the two basic tools that have been used for the creation of this typology. The tool that came out from this process is a dynamic one which means that in any case that data and real circumstances change then the classification can also change.

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

Cardiovascular disease remains the biggest cause of deaths worldwide. The percentage of premature death from this disease ranges from 4% in high income countries and 42 % in low income countries. This shows the importance of predicting heart disease at the early stage. In this paper, a new unsupervised classification system is adopted for heart attack prediction at the early stage using the patient's medical record. The information in the patient record are preprocessed initially using data mining techniques and then the attributes are classified using a Fuzzy C means classifier. In the classification stage 13 attributes are given as input to the Fuzzy C Means (FCM) classifier to determine the risk of heart attack. FCM is an unsupervised clustering algorithm, which allows one piece of data to belong to two or more clusters. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients, which gives a classification accuracy of 92%. The result shows that the proposed clustering algorithm can predict the likelihood of patients getting a heart attack in a more efficient and cost effective way than the other well known algorithms.

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

In this paper a novel object detection with the combination of fuzzy concepts and SURF is presented. This method contains several steps which are fuzzy C-mean (FCM) clustering, Gaussian filter, edge detection and speed-up robust feature (SURF). The image contains the target object which can be taken from a camera more than 2 meters away. SURF is used to detect the feature points and extract the feature descriptor that shows an excellent performance on robust and accurate object detection; however, it is prone to fail when the target object is rather far away from the camera such as the distance more than 2 meters. The proposed method addresses to solve the problem that is achieved by some pre-processing for the image as follows. Using FCM to shows up the colourful parts and using fuzzy inference system (FIS) to detect the edges in the image. The above two processes can improve the accuracy of feature detection and reduce the calculation of the descriptor in SURF. The experiment shows the proposed method performs the target extraction efficiently and accurately with the distance 2 meters between the target object and the camera.

La diversidad de teorías y corrientes del paradigma de la psicología humana, da lugar a múltiples interpretaciones y puntos de vista, en este artículo se presenta el estudio del 16pf5 y su respectiva clasificación por medio de métodos inteligentes.

This paper introduces an algorithm for image segmentation using a clustering technique; the technique is based on the fuzzy c means algorithm (FCM) that is executed iteratively with different number of clusters. Furthermore, simultaneously five validity indexes are calculated and their information is correlated to determine the optimal number of clusters in order to segment an image, results and simulations are shown in the paper.

Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy-c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient condition and clustering parameters, allowing identification of them as (local) minima of the objective function.

This paper presents the analysis, design and development of three intelligent open control systems: (1) temperature, (2) relative humidity, and (3) nutrients electrical conductivity-implemented in a sustainable greenhouse located at Tecnológico de Monterrey, Campus Ciudad de México. The intelligent controllers are based on a "Fuzzy C-Means Takagi-Sugeno" control type in order to deal with the nonlinear environmental conditions and variables inside the greenhouse. The proposed open control system was developed using an Intelligent Control Toolkit for LabVIEW™. The intelligent controls are based on an open loop control system that could be used under the boundaries of different crops. Finally, experimental results are shown in order to validate the correct performance of the controllers.

Clustering is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data. The proposed system, try to exploit computational power from the multicore processors by modifying the design on existing algorithms and software. However, the existing clustering algorithms either handle different data types with inefficiency in handling large data or handle large data with limitations in considering numeric attributes. Hence, parallel clustering has come into picture to provide crucial contribution towards clustering large data. In this paper a scalable parallel clustering algorithm called Possibilistic Fuzzy C-Means (PFCM) clustering to cluster large data is introduced. In order to harvest the full power of a multi-core processor the software application must be able to execute tasks in parallel utilizing all available CPUs. To achieve this aim, it use fork/join method in java programming. It is the most effective design techniques for obtaining good parallel performance. The experimental analysis will be carried out to evaluate the feasibility of the scalable Possibilistic Fuzzy C-Means (PFCM) clustering approach. The experimental analysis showed that the proposed approach obtained upper head over existing method in terms of accuracy, classification error percentage and time.

This paper describes a teleoperation system in which an articulated robot performs a block pushing task based on hand gesture commands sent through the Intemet. A Fuzzy C-Means clustering method is used to classify hand postures as "gesture commands". The fuzzy recognition system was tested using 20 trials each of a 12-gesture vocabulary. Results revealed an acceptance rate of 99.6% (percent of gestures with a sufficiently large membership value to belong to at least one of the designated classifications), and a recognition accuracy of 100% (the percent of accepted gestures classified correctly). Performance times to carry out the pushing task showed rapid learning, reaching standard times within 4 to 6 trials by an inexperienced operator.

In this paper, a modified version of the FCM algorithm is presented to deal with clusters with totally different geometrical properties. The proposed algorithm adopts a novel non-metric distance measure based on the idea of "point symmetry". Experimental results on several data sets are presented to illustrate its effectiveness.

Image segmentation is a growing field and it has been successfully applied in various fields such as medical imaging, face recognition, etc. In this paper, we propose a method for image segmentation that combines a region based artificial intelligence technique named fuzzy c-means (FCM) and a boundary based mathematical modeling technique level set method (LSM). In the proposed method, the contour of the image is obtained by FCM method which serves as initial contour for LSM Method. The final segmentation is achieved using LSM which uses signed pressure force (spf) function for active control of contour.

This paper presents the analysis, design and development of three intelligent open control systems: (1) temperature, (2) relative humidity, and (3) nutrients electrical conductivity-implemented in a sustainable greenhouse located at Tecnológico de Monterrey, Campus Ciudad de México. The intelligent controllers are based on a "Fuzzy C-Means Takagi-Sugeno" control type in order to deal with the nonlinear environmental conditions and variables inside the greenhouse. The proposed open control system was developed using an Intelligent Control Toolkit for LabVIEW™. The intelligent controls are based on an open loop control system that could be used under the boundaries of different crops. Finally, experimental results are shown in order to validate the correct performance of the controllers.

Medical image segmentation is a fundamental preprocessing
step in most systems that supports diagnosis or planning of surgical operations. The traditional Fuzzy c means
clustering algorithm performs well in the absence of noise.
Traditional FCM leads to its non robust result mainly due to
1. Not utilizing the spatial information in the image. 2. Use
of Euclidean distance. These limitations can be addressed by
using robust spatial kernel FCM (RSKFCM). RSKFCM consider
the spatial information and uses Gaussian kernel function to
calculate the distance between the center and data points. Though
RSKFCM gives good result, the main drawback behind this
method is the inability of generating global minima for the
objective function. To improve the efficiency of RSKFCM method, in this paper we proposed the genetic algorithm based RSKFCM. By using the genetic algorithm, RSKFCM initializes the cluster centers and reaches the global minima of the objective function.
Experimentation is carried out on the standard brain image
dataset. Experimental result reveals that the proposed genetic
algorithm based RSKFCM outperforms other FCM methods with
the use of various cluster validity functions

Fuzzy C-means (FCM) is a powerful clustering algorithm and has been introduced to overcome the crisp definition of similarity and clusters. FCM ignores the importance of features in the clustering process. This affects its authenticity and accuracy. We can overcome this problem by appropriately assigning weights to features according to their clustering importance. This paper, proposes an improved FCM algorithm based on the method proposed by Huang by automated feature weighting. The simulation results on several UCI databases show that the proposed algorithm exhibits better performance than FCM.

Errors in the scanning procedures lead to uncertainties when trying to segment the scanned images. Fuzzy c-means is a clustering method that can be applied to segment images with uncertainty estimates. Bias-corrected fuzzy c-means (BCFCM) clustering compensates for two sources of uncertainty by modeling noise and bias fields during the segmentation process. In this paper, we present an approach to improve BCFCM clustering and apply it to magnetic resonance imaging (MRI) data of the human brain. Our approach is based on two variants of BCFCM clustering, the classical one and the one with distance-based weights. We improve both variants by slightly modifying their main algorithms for better bias field estimation. To evaluate the improved algorithms, we apply the algorithms to synthetic data, simulated MRI brain data, and real MRI brain data with ground truth in form of manual segmentation. All experiment results show that our improved methods outperform the original methods in both the segmentation accuracy and efficiency (the number of iterations).

This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzy clustering problem, especially for large sizes. When the problem becomes large, the FCM algorithm may result in uneven distribution of data, making it difficult to find an optimal solution in reasonable amount of time. The PSO algorithm does find a good or nearoptimal solution in reasonable time, but its performance was improved by seeding the initial swarm with the result of the c-means algorithm. The fuzzy c-means, PSO and FPSO algorithms are compared using the performance factors of object function value (OFV) and CPU execution time. It was ascertained that the computational times for the FPSO method outperforms the FCM and PSO method and had higher solution quality in terms of the objective function value (OFV).

This paper introduces an algorithm for image segmentation using a clustering technique; the technique is based on the fuzzy c means algorithm (FCM) that is executed iteratively with different number of clusters. Furthermore, simultaneously five validity indexes are calculated and their information is correlated to determine the optimal number of clusters in order to segment an image, results and simulations are shown in the paper.