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Wireless Sensor Networks (WSN) is generally used in monitoring and controlling specific environments. Low-priced sensor nodes are used to form the WSN and are kept distributed in a dense manner in the environment. Collecting information and forwarding it to a Base Station (BS) is the chief function of a sensor node. New trends show that the importance and relevance of WSNs has widened and improved...
A novel framework to optimize the identification clustering of multipath scatterers in a MIMO wireless system is proposed. It is a comprehensive evaluation of major cluster identification methods across multiple categories of clustering methodologies. The reliability will be ensured with the use of a parameter selection framework utilizing the Bayesian Information Criterion (BIC). Statistical preprocessing...
Due to the increased market competition increased data management and analysis has landed as in an era that requires further optimization data management and analysis. Big data technologies like apache HADOOP provide a frame work for parallel data processing and generation of analyzed results. MAPREDUCE method is used for analysis of data using various data analysis algorithms like clustering, fragmentation...
Community detection algorithms are important for determining the character statistics of complex networks. Compared with the conventional community detection algorithms, which always focus on undirected networks, our algorithm is concentrated on directed networks such as the WeChat moments relationship network and the Sina Micro-Blog follower relationship network. To address disadvantages such as...
The evolution of Social networking sites has posed lot of challenges for technology firms and researchers [1]. The Social networking sites are gaining popularity amongst users across the globe and networking of individuals is increasing very rapidly. People search on the Social networking sites to find old friends and other interesting people, this search operation runs in the background of the Social...
This paper discusses a deterministic clustering approach to capacitated resource allocation problems. In particular, the Deterministic Annealing (DA) algorithm from the data-compression literature, which bears a distinct analogy to the phase transformation under annealing process in statistical physics, is adapted to address problems pertaining to clustering with several forms of size constraints...
This paper presents a hard clustering technique using fireworks algorithm with adaptive transfer function (FWAATF) for image segmentation. The fireworks algorithm (FWA) is a recently developed new Swarm Intelligence (SI) algorithm for function optimization. This algorithm simulates the process of fireworks explosion in the night sky. The main characteristic of FWA is the good balance between exploration...
A huge amount of digital data containing useful information, called Big Data, is generated everyday. To mine such useful information, clustering is widely used data analysis technique. A large number of Big Data analytics frameworks have been developed to scale the clustering algorithms for big data analysis. One such framework called Apache Spark works really well for iterative algorithms by supporting...
According to the characteristics of positive and negative edge of signed networks, a new signed networks community detection algorithm BTCN_SNCD (Signed networks Community Detection Based on the Tightness of Common Neighbors) is proposed based on the tightness of common neighbors. Firstly, in view of the shortcomings of the traditional local similarity metrics only considering the number of common...
Swarm intelligence is defined as the properties of artificial systems. It is suited to depict people's daily behavior, such as social media users' behavior. Social media users have some characteristics. For one thing, users who have the same interest will focus on the same VIP (Very Important Person) users inside the industry. For another, the users concentrating on the same VIP user may focus on...
Swarm Intelligence algorithms, in many optimization problems, have constantly served a purpose of global search method. One of the problems confronted during optimization is clustering problem. Input for a clustering process is a set of data which are then organized into a number of sub-groups. Modern studies have recommended that partitioned or segregated clustering algorithms are more appropriate...
The brain-computer interface (BCI), identify brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper identification of electroencephalogram (EEG) based BCI for motor imagery (MI) task is done through asynchronous approach. Transferring the brain computer interface (BCI) from laboratory state to real time application desires BCI...
Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm,...
In classification problems, generalization ability has a key role for successful prediction. Well known Support Vector Machine classifier, tries to increase generalization ability via maximizing the margin, which is the distance between two parallel hyperplanes on the closest points. In this work we investigate maximizing the margin on non-parallel multi surfaces, by adapting GEPSVM* to Polyhedral...
Wireless sensor networks are principally categorized by insufficient energy resource. Naturally, communication between the nodes is the utmost energy consuming act that they perform. Hence, development of a well-organized clustering algorithm can play a vital part in enhancing the lifetime of network. Currently, nature inspired methodologies are very common in dealing with it. This work presents a...
We present a unified approach for simultaneous clustering and outlier detection in data. We utilize some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. Unlike most (all) of the previous techniques, in our framework the number of clusters arises...
In clustering applications, multiple views of the data are often available. Although clustering could be done within each view independently, exploiting information across views is promising to gain clustering accuracy improvement. A common assumption in the field of multi-view learning is that the clustering results from multiple views should be consistent with a latent clustering. However, the potential...
Image segmentation is one research area of image processing which has many applications in practice. In this paper we have undertaken image segmentation problem using spatial fuzzy c means (SFCM) clustering which is an unsupervised classification scheme. A good segmentation result is desirable for classification problem especially in medical image classification. Therefore SFCM clustering result is...
Real-world datasets consist of data representations (views) from different sources which often provide information complementary to each other. Multi-view learning algorithms aim at exploiting the complementary information present in different views for clustering and classification tasks. Several multi-view clustering methods that aim at partitioning objects into clusters based on multiple representations...
In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation...
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