Fuzzy C-Means Clustering Algorithm

The rapid advancement of DNA microarray technology has revolutionalized genetic research in bioscience. Due to the enormous amount of gene expression data generated by such technology, computer processing and analysis of such data has become indispensable. In this paper, we present a computational framework for the extraction, analysis and visualization of gene expression data from microarray experiments. A novel, fully automated, spot segmentation algorithm for DNA microarray images, which makes use of adaptive thresholding, morphological processing and statistical intensity modeling, is proposed to: (i) segment the blocks of spots, (ii) generate the grid structure, and (iii) to segment the spot within each subregion. For data analysis, we propose a binary hierarchical clustering (BHC) framework for the clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the fuzzy C-means algorithm and the average linkage hierarchical clustering algorithm are used to split the data into two classes. Secondly, the Fisher linear discriminant analysis is applied to the two classes to assess whether the split is acceptable. The BHC algorithm is applied to the sub-classes recursively and ends when all clusters cannot be split any further. BHC does not require the number of clusters to be known in advance. It does not place any assumption about the number of samples in each cluster or the class distribution. The hierarchical framework naturally leads to a tree structure representation for effective visualization of gene expressions.

Social media is said to have an impact on the public discourse and communication in the society. It is increasingly being used in the political context. Social networks sites such as Facebook, Twitter and other microblogging services provide an opportunity for public to give opinions about some issues of interest. Twitter is an ideal platform for users to spread not only information in general but also political opinions, whereas Facebook provides the capability for direct dialogs. A lot of studies have shown that a need exists for stakeholders to collect, monitor, analyze, summarize and visualize these social media views. Some authors have tended to categorize these comments as either positive or negative ignoring the neutral category. In this paper, we demonstrate the importance of the neutral category in the clustering of sentiments from the social media. We then demonstrate the use of fuzzy clustering for this kind of task.

We describe the use of a binary hierarchical clustering (BHC) framework for clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the K-means algorithm is used to split the data into two classes. Secondly, the Fisher criterion is applied to the classes to assess whether the splitting is acceptable. The algorithm is applied to the sub-classes recursively and ends when all clusters cannot be split any further. BHC does not require the number of clusters to be known. It does not place any assumption about the number of samples in each cluster or the class distribution. The hierarchical framework naturally leads to a tree structure representation. We show that by arranging the BHC clustered gene expression data in a tree structure, we can easily visualize the cluster results. In addition, the tree structure display allows user judgement in finalizing the clustering result using prior biological knowledge.

The paper presents model based on fuzzy methods for churn prediction in retail banking. The study was done on the real, anonymised data of 5000 clients of a retail bank. Real data are great strength of the study, as a lot of studies often use old, irrelevant or artificial data. Canonical discriminant analysis was applied to reveal variables that provide maximal separation between clusters of churners and non-churners. Combination of standard deviation, canonical discriminant analysis and k-means clustering results were used for outliers detection. Due to the fuzzy nature of practical customer relationship management problems it was expected, and shown, that fuzzy methods performed better than the classical ones. According to the results of the preliminary data exploration and fuzzy clustering with different values of the input parameters for fuzzy cmeans algorithm, the best parameter combination was chosen and applied to training data set. Four different prediction models, called prediction engines, have been developed. The definitions of clients in the fuzzy transitional conditions and the distance of k instances fuzzy sums were introduced. The prediction engine using these sums performed best in churn prediction, applied to both balanced and non-balanced test sets.

Khóa học Luyện giải đề môn Toán -Thầy Đặng Việt Hùng Facebook: LyHung95 Tham gia trọn vẹn khóa LTĐH và Luyện giải đề tại Moon.vn để đạt được kết quả cao nhất trong kỳ TSĐH 2014! ĐỀ THI THỬ ĐẠI HỌC NĂM 2014 Môn thi: TOÁN; khối A và khối A1, lần 2 Thời gian làm bài: 180 phút, không kể thời gian phát đề

Cluster analysis is a multivariate analysis technique with the main purpose is to classify objects into groups based on the characteristics observation. Clustering methods currently being developed is the method of grouping based on the fuzzy sets, also called as fuzzy clustering analysis. Various methods of fuzzyclustering analysis, such as Fuzzy C-Means Cluster (FCM) and Fuzzy C-Shell Cluster (FCS). FCM is developed based on c-means using fuzzy weighting, while the FCS applying geometric partition, in particular using the circle and ellipse shapes. While the optimum number of groups is obtained by the criteria of validity measure, mentioned as Xie and Beni index, while for the right method is used the ratio of standard deviation within the group against the standard deviation between groups. FCM and FCS will be applied to the grouping of districts/cities by using the variable forming the Human Development Index (HDI) in 2008 for the districts/cities on the island of Jawa. HDI forming variables, consisting of the life expectancy; literacy rates, mean years school, and the Purchasing Power Parity . The results obtained FCM method has optimum number of groups is six groups, and FCS method has optimum number of groups is four groups.

— University course timetabling problem is one of the hard problems and it must be done for each term frequently which is an exhausting and time consuming task. The main technique in the presented approach is focused on developing and making the process of timetabling common lecturers among different departments of a university scalable. The aim of this paper is to improve the satisfaction of common lecturers among departments and then minimize the loss of resources within departments. The applied method is to use a collaborative search approach. In this method, at first all departments perform their scheduling process locally; then two clustering and traversing agents are used where the former is to cluster common lecturers among departments and the latter is to find unused resources among departments. After performing the clustering and traversing processes, the mapping operation in done based on principles of common lecturers constraint in redundant resources in order to gain the objectives of the problem. The problem's evaluation metric is evaluated via using fuzzy c-means clustering algorithm on common lecturer constraints within a multi agent system. An applied dataset is based on meeting the requirements of scheduling in real world among various departments of Islamic Azad University, Ahar Branch and the success of results would be in respect of satisfying uniform distribution and allocation of common lecturers on redundant resources among different departments .

Clustering plays an outstanding role in data mining research. Among the various algorithms for clustering, most of the researchers used the Fuzzy C-Means algorithm (FCM) in the areas like computational geometry, data compression and vector quantization, pattern recognition and pattern classification. In this research, a simple and efficient implementation of FCM clustering algorithm is presented. Three types of inputs are given to algorithm. The data points are first distributed manually, the statistical distributions Normal and Uniform are anther two methods by using the Box-Muller formula. The algorithm is analyzed based on their clustering quality. The behavior of the algorithm depends on the number of data points as well as on the number of cluster. The performance of the algorithm is investigated during different execution of the program on the input data points. The execution time for each cluster and total elapsed time to cluster all the data points is also analyzed and the r...

In this paper an effective dynamic video summarisation algorithm is presented using audio-visual features extracted from videos. Audio, colour and motion features are dynamically fused using an adaptively weighting mechanism. Dissimilarities of temporal video segments are formulated using the extracted features before these segments are clustered using a fuzzy c-means algorithm with an optimally determined cluster number. The experimental results demonstrate the ability of the proposed algorithm to automatically summarise the videos with good performance.

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.

Two-mode partitioning is a relatively new form of clustering that clusters both rows and columns of a data matrix. In this paper, we consider deterministic two-mode partitioning methods in which a criterion similar to k-means is optimized. A variety of optimization methods have been proposed for this type of problem. However, it is still unclear which method should be used, as various methods may lead to non-global optima. This paper reviews and compares several optimization methods for two-mode partitioning. Several known methods are discussed, and a new fuzzy steps method is introduced. The fuzzy steps method is based on the fuzzy c-means algorithm of Bezdek (1981) and the fuzzy steps approach of Heiser and Groenen (1997) and Groenen and Jajuga (2001). The performances of all methods are compared in a large simulation study. In our simulations, a two-mode k-means optimization method most often gives the best results. Finally, an empirical data set is used to give a practical example of two-mode partitioning.

Diabetes is a serious health issue faced by the society. Classification of tissues in a diabetic wound is highly essential to monitor the healing progress of the wound. The standard manual tissue classification methods utilized by the clinicians are prone to inaccuracy and is highly dependant on the knowledge and experience of the expert. Moreover, since the wounds may form at a body part that is difficult to access, assessing of the wound becomes time-consuming and causes distress to the patient. Hence, an automatic diabetic wound classification system is required for accurate assessment of the wound. In this paper, we offer tissue classification system for diabetic ulcers that utilizes Fuzzy C-Means and colour analysis using colour features to cluster the digital images. The clusters are initially labelled by calculating the minimum Euclidean Distance of the cluster center from the reference points. Each cluster was further analyzed by using spatial relation to label the smaller blobs, and tissue relation to label larger blobs. Granulation and Epithelial were further evaluated by computing the shiny regions caused by the reflection of light on wetter regions of the wound. The proposed algorithm will segment wound tissues into granulation, slough, eschar, and epithelial tissues and display the segmented regions along with the percentage area of each type of tissue. This system can be used by the diagnostician to effectively and easily monitor the healing progress of the wound.

... This proposed algorithm has the advantages of both the sequential FCM and parallelFCM for the clustering process in the segmentation techniques. This algorithm is very fast when the image size is large and it requires less execution time. ...

A membership function and an objective function deduced from the geometrical properties associated to the metric defined by the covariance matrix of a sample have been proposed recently. We use these functions to determine the membership probabilities and the criterion function in the fuzzy C-means algorithm.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

Cloud computing is Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Cloud computing is the hottest purpose built architecture created to support computer users. The cloud addresses three main areas of operation SaaS (software-as-a-service),PaaS (platform-as-a-service) and IaaS (infrastructure as a service). The large amount of data can be stored into cloud Data centers with low cost. We Integrate Data Mining and Cloud Computing to provide a quick access to this large volume amount of data on cloud. This paper aimed to study Clustering algorithm which can be applicable in cloud computing. This paper also describes the role of soft clustering in Cloud computing environment.

Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The syst...

Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called a partition coefficient and exponential separation (PCAES) index. It uses the factors from a normalized partition coefficient and an exponential separation measure for each cluster and then pools these two factors to create the PCAES validity index. Considerations involving the compactness and separation measures for each cluster provide different cluster validity merits. In this paper, we also discuss the problem that the validity indexes face in a noisy environment. The efficiency of the proposed PCAES index is compared with several popular validity indexes. More information about these indexes is acquired in series of numerical comparisons and also three real data sets of Iris, Glass and Vowel. The results of comparative study show that the proposed PCAES index has high ability in producing a good cluster number estimate and in addition, it provides a new point of view for cluster validity in a noisy environment.

Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.

Abstract -- Clustering is one kind of unsupervised learning methods. K-mediods is one of the partitioning clustering algorithms and it is also a distance based clustering. Distance measure is an important component of a clustering algorithm to measure the distances between data points. In this thesis paper, a comparison between Euclidean distance function and Manhattan distance function by using K-mediods has been made. To make this comparison, an instance of seven objects of a data set has been taken. Finally, we will show the simulation results in the result section of this paper.

Keywords-- Clustering, K-mediods, Manhattan distance function, Euclidean distance function.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 13, No. 10, October 2015

https://sites.google.com/site/ijcsis/
ISSN 1947-5500

A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which suggest usage of different resources of the server. Feature selection algorithms have been used to extract the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using an esxtop command. Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index respectively. The best cluster is further identified by the determined indices. The features of the best cluster are considered into a set of optimal parameters.

Clustering is a technique of grouping similar data objects in one group and dissimilar data objects in other group. Clustering or data grouping is the key technique of the data mining. It is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data. Grouping of data into clusters aims to maximize the intra class similarity and also minimize the inter class similarity. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. This paper aims to provide a brief overview and complexities of various clustering algorithms.

This paper proposes a definition of a fuzzy partition element based on the homomorphism between type-1 fuzzy sets and the three-valued Kleene algebra. A new clustering method based on the C-means algorithm, using the defined partition, is presented in this paper, which will be validated with the traditional iris clustering problem by measuring its petals.

The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. The analysis of medical image is directly based on four steps: 1) image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by pattern recognition system or classifier. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semisupervised approach in which supervision is involved only at the level of defining texture-primitive cell; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. The algorithm was first tested on Markov textures, and the success rate achieved in classification was 100%; further, the algorithm was able to give results on the test images impregnated with distorted Markov texture cell. In addition to this, the output also indicated the level of distortion in distorted Markov texture cell as compared to standard Markov texture cell. Finally, algorithm was applied to selected medical images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated. a) image filtering or preprocessing, b) image segmentation, c) feature extraction, and d) classification or analysis of extracted features by classifier or pattern recognition system. The image filtering or preprocessing depends mainly on the quality of the image acquired from image acquisition system. The main aim of image preprocessing is to suppress unwanted noise and to enhance image features important from further analysis point of view, and is most of the time specific in nature depending upon the type of noise present in the image. (For example, in case of image with poor 'brightness and contrast,' histogram equalization can be used to improve the brightness and contrast of an image.) In analysis of medical images, we try to avoid image preprocessing unless and until it is very much necessary as image preprocessing typically decreases image information content.

International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.

To enhance the quality of education system, student performance analysis plays an important role for decision support. Evaluation of student's performance is an important aspect in every institution. The student's knowledge about the programming concepts can be tested using various forms of assessments. It helps the teacher to identify students who need special attention and allow the teacher to provide appropriate guidance. To achieve this a machine learning approach called the Recursive Clustering technique is used to group the students of the programming course into groups based on their performance in the given assessment. There are three groups: Pass, Fail and Above Average. More attention is given to the students present in lower group as they are prone to fail. Study materials are provided to the students for preparation. Tests are conducted for the students who fail. The process is repeated for three times so that most of the students from the lower group will move to the higher group. The results are compared using k-means algorithm. The results prove that this approach provides an effective way to predict the low performing students from their assessment, thereby enabling the student to be on track.

Abstrack-Processing information of the most disease suffered by the people in a region, particularly to those receive Health Insurance card (Jaminan Kesehatan Daerah; JAMKESDA) was one of the government focuses. The study used Fuzzy C-Means (FCM), to process the disease data suffered by JAMKESDA users for four years in regional public hospital. The results showed that FCM method could group the disease into two forms namely generative and infectious one. Based on the cluster validity test using Partition Coefficient (PC), the partition value of fuzzy is higher by using two clusters (0.68) rather than three clusters. In conclusion, the forming model of two clusters is more optimal than that three one to process data disease of JAMKESDA users. Abstrak-Pengolahan informasi mengenai penyakit yang paling banyak diderita oleh masyarakat disuatu daerah, khususnya bagi masyarakat yang menerima pelayanan berupa kartu Jaminan Kesehatan (JAMKESDA) dari pemerintah merupakan salah satu hal yang menjadi fokus perhatian pemerintah. Penelitian ini menggunakan metode Fuzzy C-Means (FCM), untuk mengolah data penyakit yang diderita oleh masyarakat pengguna JAMKESDA dalam periode 4 tahun di rumah sakit umum daerah. Adapun hasil penelitian menunjukkan bahwa metode FCM mampu mengelompokkan penyakit ke dalam dua jenis kelompok penyakit yaitu degeneratif dan infeksi. Dimana, berdasarkan hasil uji validitas cluster dengan menggunakan metode Partition Coefficient (PC), menghasilkan nilai partisi fuzzy lebih besar menggunakan dua cluster (sebesar 0.68) dibandingkan tiga cluster, sehingga model pembentukan cluster dengan dua cluster lebih optimal dibanding dengan tiga cluster untuk pengolahan data penyakit pada masyarakat pengguna JAMKESDA.

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.

A capacidade de estabelecer comparações e perceber diferenças e semelhanças entre objetos, fenômenos e pessoas é uma importante habilidade cognitiva que permite organizar informações, reconhecer padrões e estabelecer relações entre variáveis. Tal habilidade é natural ao ser humano e vem sendo desenvolvida e aplicada desde tempos imemoriais, por exemplo, na taxonomia das espécies. A análise de agrupamentos consiste na algoritmização desta habilidade cognitiva com o objetivo de identificar automaticamente grupos de objetos com caraterísticas semelhantes em uma massa de dados. Ao longo do minicurso pretende-se apresentar os principais algoritmos hierárquicos (métodos de encadeamento e Ward) e não hierárquicos (K Means) para análise de agrupamentos e abordagens baseadas em conceitos de machine learning como o mapa auto organizável, o método fuzzy c-means e o DBSCAN (Density-based spatial clustering of applications with noise). Algumas aplicações reais também serão ilustradas com o auxílio do programa R.
http://www.semest.uff.br/index.php/minicursos

In data mining, the conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. Fuzzy clustering methods have the potential to manage such situations efficiently. This paper introduces the limitations of conventional clustering methods through k-means and fuzzy c-means clustering and demonstrates the drawbacks of the algorithms in handling outlier points. In this paper, we propose a new fuzzy clustering method which is more efficient in handling outlier points than conventional fuzzy c-means algorithm. The new method excludes outlier points by giving them extremely small membership values in existing clusters while fuzzy c-means algorithm tends give them outsized membership values. The new algorithm also incorporates the positive aspects of k-means algorithm in calculating the new cluster centers in a more efficient approach than the c-means method.

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm is capable of accurately predicting stock market return. We attempt to model and predict the return on stock price index of the Istanbul Stock Exchange (ISE) with ANFIS. We use six macroeconomic variables and three indices as input variables. The experimental results reveal that the model successfully forecasts the monthly return of ISE National 100 Index with an accuracy rate of 98.3%. ANFIS provides a promising alternative for stock market prediction. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.

In this article, we propose clustering approach based on Principal Component Analysis (PCA) to diagnosis of heart disease patients. At the first stage, the original dataset is reduced using PCA reduction method. Then, at the second stage, reduced dataset is applied to clustering methods which is based on fuzzy C-means and K-means algorithms. These algorithms are implemented and tested on a Cleveland heart disease dataset. We compared the clustering results with and without PCA. The results are suggesting that the combination of clustering algorithms and PCA was the most effective at heart disease diagnosis.

Remote sensing images are predominantly affected by the presence of mixed pixels. Soft classifiers have the
advantage to handle the mixed pixels due to the shortcomings of hard classifiers. The fuzzy based classifiers
have shown to be robust and accurate when classifying land use and land cover maps. In the literature, the
fuzzy c- means classifier has been studied with Euclidean, Mahalanobis and diagonal Mahalanobis norms. In
this study, the fuzzy c- means classifier has been studied with nine other similarity and dissimilarity measures:
Manhattan distance, chessboard distance, Bray-Curtis distance, Canberra, Cosine distance, correlation
distance, mean absolute difference, median absolute difference and normalised squared Euclidean distance.
Both single and composite modes were used with a varying weighted constant (m) at different α-cuts.
Formosat-2 image and Landsat-8 image of 8m and 30m spatial resolution were used to implement the
weighted norms respectively. Formosat-2 image of finer resolution was used as the reference image for the
accuracy assessment of Landsat-8 image of coarser resolution. The results showed that the best single and
composite norms were obtained by optimizing the weighted constant (m). This helps in controlling the
degree of fuzziness at various α-cuts. The two best single norms obtained were combined to study the effect
of composite norms on the datasets used. An image to image accuracy check was conducted to assess the
accuracy of the classified images. Fuzzy Error Matrix (FERM) was used to measure the accuracy assessment
outcomes for Landsat-8 dataset with respect to Formosat-2 dataset. Cosine norm was found to be the best
single norm among all the norms with an overall accuracy of 75.24%, followed by the Euclidean norm.
These two norms were combined to form the composite norm which showed an overall accuracy of 69.80%.
The accuracy of the classification was also measured in the case of an untrained class (wheat), which resulted
in a decrease in the overall accuracy in comparison to the trained case. To conclude FCM classifier with
Cosine norm performed better than the conventional Euclidean norm. But, due to the incapability of FCM
classifier to handle noise properly, the classification accuracy was around 75%.

Clustering is a technique used in network routing to enhance the performance and conserve the network resources. This paper presents a cluster-based routing protocol for VANET utilizing a new addressing scheme in which each node gets an address according to its mobility pattern. Hamming distance technique is used then to partition the network in an address-centric manner. The simulation results show that this protocol enhances routing reachability, whereas reduces routing end-to-end delay and traffic received comparing with two benchmarks namely AODV and DSDV.

In this paper, we propose a new approach to fuzzy clustering in order to handle the uncertainties in pattern recognition problems on the basis of conventional fuzzy C-means algorithm (FCM). In our approach, we define the concept of linguistic cluster center by employing the semantic structure of hedge algebra. This kind of cluster center is constructed to give the appropriate weights for each pattern of the dataset in our clustering algorithm. The parameters of hedge algbra are then optimized in the training process to obtain the suitable parameters for the dataset. We also incorporate the k-means algorithm to get better results in comparing to conventional FCM.

This paper studies and synthesis the parallel distribution compensation PDC controller for discretetime
nonlinear systems that which is represented by Takagi-Seguno (T-S) fuzzy models. This work is based on
two steps: the first one is modeling and identification of the studied systems by using the clustering method.
In particular, we use Fuzzy C-Means (FCM) algorithm. In second one, we interest in the first part to the stability
analysis of multi-model by Lyapunov functions using convex optimization algorithms at the linear matrix
inequality LMI and in the second part, we focus on synthesis of PDC control law. Finally, the study
methodology is implemented on an inverted pendulum system.

In this paper, we present an automated method to segment the breast masses in Mammograms that can be acquired in the routine clinical setting. Breast cancer is the most common deadly disease among the women. But early detection can save their life. Mass detection is an active research area for the past few years but till now no accurate method has been devised. In this paper an automated method to segment the breast masses has been proposed based on the Fuzzy C-Means Clustering method. The results show that this method can better assist as a tool in detection of breast cancer.

Image segmentation plays an important role in image analysis. It is one of the first and most important tasks in image analysis and computer vision. This proposed system presents a variation of fuzzy c- means algorithm that provides image clustering. Based on the Mercer kernel, the kernel fuzzy c-means clustering algorithm (KFCM) is derived from the fuzzy c-means clustering algorithm (FCM).The KFCM algorithm that provides image clustering and improves accuracy significantly compared with classical fuzzy C-Means algorithms. This proposed system makes the use of the advantages of KFCM and also incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called Generalized Spatial Kernel based Fuzzy C- Means (GSKFCM) algorithm. The major characteristic of GSKFCM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation as well as it is paramete...

Energy efficiency is an essential issue to be reckoned in wireless sensor networks development. Since the low-powered sensor nodes deplete their energy in transmitting the collected information, several strategies have been proposed to investigate the communication power consumption, in order to reduce the amount of transmitted data without affecting the information reliability. Lossy compression is a promising solution recently adapted to overcome the challenging energy consumption, by exploiting the data correlation and discarding the redundant information. In this paper, we propose a hybrid compression approach based on two dimensions specified as horizontal (HC) and vertical compression (VC), typically implemented in cluster-based routing architecture. The proposed scheme considers two key performance metrics, energy expenditure, and data accuracy to decide the adequate compression approach based on HC-VC or VC-HC configuration according to each WSN application requirement. Simulation results exhibit the performance of both proposed approaches in terms of extending the clustering network lifetime.

The current study investigated a median filter with the fuzzy level set method to propose fuzzy segmentation of magnetic resonance imaging (MRI) cerebral tissue images. An MRI image was used as an input image. A median filter and fuzzy c-means (FCM) clustering were utilized to remove image noise and create image clusters, respectively. The image clusters showed initial and final cluster centers. The level set method was then used for segmentation after separating and extracting white matter from gray matter. Fuzzy c-means was sensitive to the choice of the initial cluster center. Improper center selection caused the method to produce suboptimal solutions. The proposed algorithm was successfully utilized to segment MRI cerebral tissue images. The algorithm efficiently performed segmentation of test MRI cerebral tissue images compared with algorithms proposed in previous studies.

One of the crucial problems in the field of functional genomics is to identify a set of genes which are responsible for a particular cellular mechanism. The current work explores the usage of a multi-objective optimization based genetic clustering technique to classify genes into groups with respect to their functional similarities and biological relevance. Our contribution is twofold: firstly a new quality measure to compute the goodness of gene-clusters namely protein-protein interaction confidence score is developed. This utilizes the confidence scores of the protein-protein interaction networks to measure the similarity between genes of a particular cluster with respect to their biochemical protein products. Secondly, a multi-objective based clustering approach is developed which intelligently uses integrated information of expression values of microarray dataset and protein protein interaction confidence scores to select both statistically and biologically relevant genes. For that very purpose, some biological cluster validity indices, viz. biological homogeneity index and protein-protein interaction confidence score, along with two traditional internal cluster validity indices, viz. fuzzy partition coefficient and Pakhira-Bandyopadhyay-Maulik-index, are simultaneously optimized during the clustering process. Experimental results on three real-life gene expression datasets show that the addition of new objective capturing protein-protein interaction information aids in clustering the genes as compared to the existing techniques. The observations are further supported by biological and statistical significance tests.