Fuzzy Based Clustering Algorithm Research Articles

Energy centric reputation index and fuzzy-based clustering for wireless sensor networks

Energy conservation is essential for a wireless sensor network to enhance its network lifetime. Clustering is one of the most important energy conservation techniques that prolong the WSN by providing balanced energy depletion across the network. Various clustering strategies have been proposed in the past to enhance the efficiency of the network till now. In the last two decades, fuzzy inference system based clustering solution has been proven to provide promising solutions. The existing fuzzy-based clustering algorithms consider the naïve network parameters as input that saves only a limited amount of energy in the network. However, in wireless sensor networks, the requirement is to make them highly energy efficient through an effective clustering strategy. To achieve effective clustering there is a need for energy-centric network parameters for the clustering instead of naïve network parameters. In this article, a novel energy-centric reputation index is proposed in which the naïve network parameters are unified with energy parameters in the background. Further, the reputation index scores are passed into a fuzzy-based approach to obtain the fitness value of the node chosen as the cluster head. Thus, in this paper, an energy-centric reputation index and fuzzy-based clustering(ECRIF) model is proposed for wireless sensor networks. The proposed model provides a highly energy-efficient wireless sensor network and is designed to work in two phases. In phase 1, three reputation indexes concerning internodal distance, distance to the sink, and the degree of the node is calculated while considering their energy consumption pattern in the background. In phase 2, the three reputation indexes are passed onto the fuzzy system as fuzzy input parameters to obtain the fitness value of the nodes for cluster head selection. Simulation results confirm that the proposed protocol enhances the overall lifetime of the network on an average by 70% in terms of the first node dies, 61% in terms of half node dies, and 78% in terms of the last node dies when compared with two other existing clustering models.

A Fast Fuzzy Clustering Algorithm for Complex Networks via a Generalized Momentum Method

Complex networks have been widely adopted to represent a variety of complicated systems. Given a complex network, it is of great significance to perform accurate clustering for better understanding its intrinsic organization. To this end, a fuzzy-based clustering algorithm, i.e., FCAN, has been developed. Though effective, FCAN suffers from the disadvantage of slow convergence, which in return constrains its efficiency. To address this issue, this article proposes a fast fuzzy clustering algorithm, namely, F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CAN, which incorporates a generalized momentum method into FCAN. Its fast convergence is rigorous justified in theory. Empirical studies on five datasets from real applications demonstrate that F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CAN achieves a better performance when compared with FCAN and several state-of-the-art clustering algorithms in terms of convergence rate and clustering accuracy simultaneously. Hence, F <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CAN has potential for addressing the clustering analysis of large-scale complex networks emerging from industrial applications.

Fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking

The Fuzzy C-means (FCM) clustering algorithm is an effective method for image segmentation. Non-local spatial information considers more redundant information of the image thus is more robust to noise. However, under-segmentation of non-local spatial information may exist with higher noise density. The number of iteration steps is also significant in FCM, and employing membership linking can effectively reduce the number of iteration steps. Nonetheless, when there are outliers in the membership degree, the membership linking can make the algorithm converge prematurely before reaching the optimum, affecting segmentation performance. This paper presents a fuzzy subspace clustering noisy image segmentation algorithm with adaptive local variance & non-local information and mean membership linking (FSC_LNML). Firstly, local variance templates are utilized to eliminate the under-segmentation of non-local information, and local variance & non-local information are integrated into the FCM objective function to improve robustness. Secondly, the mean membership linking is employed as the denominator of the objective function to reduce the number of iterations and solve the problem that the algorithm converges early before reaching the optimum when the membership has an outlier. Thirdly, the absolute intensity difference between the original image and the local variance & non-local information and its inverse are used to adaptively constrain the original image and the local variance & non-local information. Finally, the concept of the subspace is introduced to adaptively assign appropriate weights to each dimension of the image to improve the segmentation performance of color images. The simulation results on noisy grayscale images and noisy color images show that the efficiency of the proposed method FSC_LNML is better than other fuzzy-based clustering algorithms. The convergence proof of the algorithm is also presented.

EeFFA/DE - a fuzzy-based clustering algorithm using hybrid technique for wireless sensor networks

eeFFA/DE - a fuzzy-based clustering algorithm using hybrid technique for wireless sensor networks

Software restructuring models for object oriented programming languages using the fuzzy based clustering algorithm

Advances in the domain of software-based technology pave the way for widespread use of object-oriented programs. There is a need to develop a well-established software system that will reduce maintenance costs and enhance the usability of the component. While designing a software system, its internal structure deteriorates due to prolonged or delayed maintenance activities. In such situations, restructuring of the software is a superior approach to improve the structure without changing external behaviour of the system. One approach to carry out restructuring is to use refactoring on the existing source code for improving the internal structure of the code. Code refactoring is an effective technique for software development that improves the software’s internal structure without changing its external behaviour. The purpose of refactoring is to improve the cohesion of existing code and minimize coupling in the existing module of a software system. Among numerous methods, clustering is one of the effective approaches to increase the cohesion of the system. Hence in this paper, the authors suggest to extract member functions and member variables and propose to find their similarity by Frequent Usage Pattern approach. Next, the proposed fuzzy based clustering algorithm perform effective code refactoring. The proposed method utilizes multiple refactoring methods to increase the cohesion of the component without any change in the meaning of the software system. The proposed system will offer automated support to change low-cohesive to high-cohesive functions. Finally, the proposed model has been experimentally tested with object-oriented programs.

DATA STORING AND RETRIEVAL METHOD IN BIG DATA USING FUZZY BASED SCALABLE CLUSTERING ALGORITHMS

A massive volume of digital data holding valuable information, called Big Data, is produced each generation. To process and excavate such valuable information, clustering is commonly used as a data investigation technique. A huge amount of Big Data diagnostics contexts have been established to measure the clustering procedures used for big data analysis. There exists one and only framework called Fuzzy based mechanism which actually fits in for iterative method by associate in storage divisions and accessible. The proposed algorithm is motivated towards the design and implementation of fuzzy based clustering algorithms on big data, which could be present for clustering huge datasets due to their low computational necessities. In this paper, we propose Random Data Storing with Optimization Fuzzy Logic algorithm (RDS-FLA) applied on cluster data to handle the tasks that are connected with big data clustering. Experimental trainings data taking place several big datasets have been showed. The performance of RDS-FLA is tried in evaluation with the proposed scalable form of the temporal fuzzy and Random data storing that is implemented on the big data cluster. The computation outcomes are recounted in terms of time and space complexity, run time and measure of clustering quality, showing that RDS-FLA is able to run in much less time without compromising the clustering quality. Thus, the algorithm proposed alleviates the processing time and increases the security of storage data effectively. Advantages such as cost optimization and efficiency in data security can be identified from the experimental results of proposed algorithm.

Robust intrusion detection system based on fuzzy C means clustering scheme implemented in IoT-based wireless sensor networks

Research in wireless networks and communication protocols have reached new heights in recent times with the advent of state-of-the-art communication techniques and gadgets. They have found widespread utility in recent times especially with the advent of cloud computing and internet of things (IoT). IoTs offer immense potential in today's scenario aiding in smart computing and automation of things focused to making life of consumers easier and simpler. However, security of information transmitted and received over wireless networks is an essential concern to preserve privacy and authenticity of information. This research issue is however quite complex as it involves security of bulk information quite private and confidential to be delivered in a wireless medium prone to hackers. This research article has investigated the various issues related to security of IoT-based wireless sensor networks and proposed a fuzzy-based clustering algorithm to effectively detect an intrusion and make the information secure.

Robust intrusion detection system based on fuzzy C means clustering scheme implemented in IoT-based wireless sensor networks

Research in wireless networks and communication protocols have reached new heights in recent times with the advent of state-of-the-art communication techniques and gadgets. They have found widespread utility in recent times especially with the advent of cloud computing and internet of things (IoT). IoTs offer immense potential in today's scenario aiding in smart computing and automation of things focused to making life of consumers easier and simpler. However, security of information transmitted and received over wireless networks is an essential concern to preserve privacy and authenticity of information. This research issue is however quite complex as it involves security of bulk information quite private and confidential to be delivered in a wireless medium prone to hackers. This research article has investigated the various issues related to security of IoT-based wireless sensor networks and proposed a fuzzy-based clustering algorithm to effectively detect an intrusion and make the information secure.

Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation

This paper presents a fuzzy clustering algorithm, where local contextual information and a Gaussian function are incorporated into the objective function, for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. In doing so, for each pixel, we define a local contextual information, which actually defines its association among the other neighboring pixels based on intensity distribution. In particular, this information defines the possibility of the pixel to belong into a specific tissue type. Whereas, for each tissue region, a Gaussian surface is defined to estimate the intensity inhomogeneity (IIH) using the local image gradients, which are believed to be caused by the IIH. We use this Gaussian surface to compensate the effect of IIH. In addition, for each pixel, we have introduced global and local membership functions, which in combined in association with the other parameters are responsible for generation of cluster prototypes. The IIH of the entire image region is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on two benchmarks brain MR image databases and four volumes of real-patient brain MR image data show its efficiency and superiority over other fuzzy-based clustering algorithms.

Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering

Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like $$V_{CWB}$$ and $$V_{OS}$$ and evolutionary fuzzy based clustering algorithms. The results show that proposed method achieves the global optimal value of m, c with a minimum value of fitness function and shows significant improvement in the convergence times (the number of iterations) as compared to the state-of-the-art methods.

Fuzzy Clustering in a Complex Network Based on Content Relevance and Link Structures

Many real-world problems can be represented as complex networks with nodes representing different objects and links between nodes representing relationships between objects. As different attributes can be considered as associating with different objects, other than nontrivial link structures, complex networks also contain rich content information, and it can be a big challenge to find interesting clusters in such networks by fully exploiting the knowledge of both content and link information in them. Although some attempts have been made to tackle this clustering problem, few of them have considered the feasibility of identifying clusters in complex networks using a fuzzy-based clustering approach. We believe that, if the degree of membership to a cluster that a node belongs to can be considered, we will be able to better identify clusters in complex networks, as we may be able to identify overlapping clusters. In this paper, we, therefore, propose a fuzzy-based clustering algorithm for this task. The algorithm, which we call Fuzzy Clustering Algorithm for Complex Networks (FCAN), can discover clusters by taking into consideration both link and content information. It does so by first processing the content information by introducing a measure to quantify the relevance of contents between each pair of nodes within the network. It then proceeds to leverage the link information in the clustering process by considering a measure of cluster density. Based on these measures, FCAN identifies fuzzy clusters that are more densely connected and more highly relevant in their contents to optimize the degrees of memberships of each node belonging to different clusters. The performance of FCAN has been evaluated with several synthetic and real datasets involving those of document classification and social community detection. The results show that, in terms of accuracy, computation efficiency, and scalability, FCAN can be a very promising approach.

Brain MRI Medical Image Segmentation Using Fuzzy Based Clustering Algorithms

Brain MRI Medical Image Segmentation Using Fuzzy Based Clustering Algorithms

A Novel Fuzzy based Clustering Algorithm for Text Classification

Due to the flourish of World Wide Web and the rapid development of the Internet technology, the increasing volume of digital textual data become more and more unmanageable, therefore the importance of text classification has gained significant attention. Text classification pose some specific challenges such as high dimensionality with each document (data point) having only a very small subset of them and representing multiple labels at the same time. Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. Many researchers worked on Feature Clustering for efficient text classification. Recently a Fuzzy based feature clustering was proposed in which Gaussian distribution is used for fuzzy membership function for clustering. But the problem of skewness may occur with this distribution. To overcome that we propose an efficient Fuzzy similarity based membership function for efficient clustering and with this proposed algorithm satisfactory results obtained.

Fuzzy based clustering algorithm for privacy preserving data mining

Sharing of data among multiple organisations is required in many situations. The shared data may contain sensitive information about individuals which if shared may lead to privacy breach. Thus, maintaining the individual privacy is a great challenge. In order to overcome the challenges involved in data mining, when data needs to be shared, privacy preserving data mining (PPDM) has evolved as a solution. The objective of PPDM is to have the interesting knowledge mined from the data at the same time to maintain the individual privacy. This paper addresses the problem of PPDM by transforming the attributes to fuzzy attributes. Thus, the individual privacy is also maintained, as one cannot predict the exact value, at the same time, better accuracy of mining results is achieved. ID3 and Naive Bayes classification algorithms over three different datasets are used in the experiments to show the effectiveness of the approach.

A modified fuzzy clustering for documents retrieval: application to document categorization

The paper advocates the use of a new fuzzy-based clustering algorithm for document categorization. Each document/datum will be represented as a fuzzy set. In this respect, the fuzzy clustering algorithm, will be constrained additionally in order to cluster fuzzy sets. Then, one needs to find a metric measure in order to detect the overlapping between documents and the cluster prototype (category). In this respect, we use one of the interclass probabilistic reparability measures known as Bhattacharyya distance, which will be incorporated in the general scheme of the fuzzy c-means algorithm for measuring the overlapping between fuzzy sets. This enables the introduction of fuzziness in the document clustering in the sense that it allows a single document to belong to more than one category. This is in line with semantic multiple interpretations conveyed by single words, which support multiple membership to several classes. Performances of the algorithms will be illustrated using a case study from the construction sector.

Application of an Autonomous Agent Network to Support the Architecture of a Holonic Manufacturing System

To remain a competitive force in the world market, manufacturing enterprises must design and produce new products in an effective way. To reduce the product launching time, manufacturing enterprises must be versatile, open to changes, and capable of designing and modifying their own facilities and processes efficiently for the design of new products. In this context, the concept of autonomous, adaptive, cognitive and cooperating entities known as holons is conceived which leads to the evolution of a holonic manufacturing system (HMS) where highly distributed control paradigms are adopted to alleviate the problems related to frequent process disturbances. In order to streamline the functioning of an HMS, it is necessary to form an efficient, flexible and responsive network of agents, which are intra-holonic entities that inherit the same characteristics as the holons. This network of agents can be termed an autonomous agent network. The agent is formed by the parties, which are the functional units of the holonic manufacturing system. The aim of this paper is to specify the communication protocols and subsequently synthesise and cluster the individual parties into autonomous agents in accordance with the basic constraints of a holonic manufacturing system. Here a fuzzy c-means clustering algorithm is proposed to club the parties to capture effectively the uncertainty and imprecision associated with them. Besides the grouping of the parties to form agents, the proposed fuzzy-based clustering algorithm ensures that the agents formed are more amenable to the dynamic environment prevailing on the shop floor of present day automated manufacturing systems and thus makes the essence of a holonic manufacturing system successful. Keeping in mind the imprecision, uncertainty, and conflicting nature of objectives, the proposed approach aptly models the problem, and its applicability is exemplified by a test problem.