Modified Clustering Algorithm Research Articles

A Modified Clustering Algorithm in WSN

Nowadays many applications use Wireless Sensor Networks (WSN) as their fulfill the purpose of collection of data from a particular phenomenon. Their data centric behavior as well as harsh restrictions on energy makes WSN different from many other networks known. During this work the energy management problem of WSN is studied, by using our proposed modified algorithm. It is a clustering algorithm, where nodes are organized in clusters and send their data to a shift selected cluster head. It will provide improvement on energy consumption in terms of member nodes, by making cluster heads static. LEACH already has a good energy saving strategy but our modification will provide an easier approach towards efficiency.

A novel hybrid CDL-based multipath propagation model for the high-speed railway at 2.35 GHz

In this study, based on the actual channel measurements of the high-speed railway (HSR) in a viaduct scenario at 2.35 GHz, a hybrid cluster delay line (CDL) channel modeling technology which combines a statistical model and a theoretical model is proposed. This novel channel-characterization approach is used to reveal the statistical properties for “random” scattering and to provide a geometric mechanism for computing the time-variant paths for “periodically moving” scattering. Using a modified clustering algorithm in the delay domain, the inter-cluster and intra-cluster characteristics against the distance between the transmitter and the receiver were extracted and analyzed. In addition, the measurement results were compared with some conclusions from other measurement studies, and the proposed channel model was validated by comparing the simulated results with the measurement data. The study results provide a useful technique for the evaluation and verification of wireless communications in the HSR environment.

Data analysis of complex objects using a modified clustering algorithm

At the present moment, the development of universal and reliable methods and approaches suitable for processing information from various fields, including the solution of problems that may arise in the medical field, is an urgent problem. In the treatment of complex diseases of the musculoskeletal system, whose etiology is not fully disclosed and requires additional investigation, is no exception. As a result of the analysis, it was concluded that for solving such kind of problems with ambiguous, variable data it makes sense to use a modified clustering algorithm.The algorithm allows to apply specific, the most suitable method for current data at each stage of the study. The study of the final stage of the algorithm – integration of similar classes for obtaining the final partition.The idea of considering a complex object − the musculoskeletal system appeared as the result of analyzing specific articles of the complex object.As a result of the studies it was concluded that the modified clustering method with integrating similar classes for obtaining the final partition makes sense to use in experiments with a complex object − the musculoskeletal system. Experimental data will be presented with the development of the problem under consideration.

Robust c-prototypes algorithms for color image segmentation

Abstract In this paper, we present a modified clustering algorithm to segment color images. The proposed technique is based on the robust c-prototypes algorithm with some modifications in its objective function and updating equations. The JK chromatic subspace of the IJK color space is used to segment color images. The algorithm performance is tested on real images with natural artifacts that make the segmentation process difficult. Additionally, a remote sensing image is segmented to demonstrate that the proposed algorithm can be used in real applications. Simulation results indicate that the proposed method is more effective than others proposed in the literature in terms of objective and subjective criteria.

The impact of cluster representatives on the convergence of the K-modes type clustering

As a leading partitional clustering technique, k-modes is one of the most computationally efficient clustering methods for categorical data. In the k-modes, a cluster is represented by a "mode," which is composed of the attribute value that occurs most frequently in each attribute domain of the cluster, whereas, in real applications, using only one attribute value in each attribute to represent a cluster may not be adequate as it could in turn affect the accuracy of data analysis. To get rid of this deficiency, several modified clustering algorithms were developed by assigning appropriate weights to several attribute values in each attribute. Although these modified algorithms are quite effective, their convergence proofs are lacking. In this paper, we analyze their convergence property and prove that they cannot guarantee to converge under their optimization frameworks unless they degrade to the original k-modes type algorithms. Furthermore, we propose two different modified algorithms with weighted cluster prototypes to overcome the shortcomings of these existing algorithms. We rigorously derive updating formulas for the proposed algorithms and prove the convergence of the proposed algorithms. The experimental studies show that the proposed algorithms are effective and efficient for large categorical datasets.

Information Theoretic Learning and local modeling for binary and multiclass classification

In this paper, a learning model for binary and multiclass classification based on local modeling and Information Theoretic Learning (ITL) is described. The training algorithm for the model works on two stages: first, a set of nodes are placed on the frontiers between classes using a modified clustering algorithm based on ITL. Each of these nodes defines a local model. Second, several one-layer neural networks, associated with these local models, are trained to locally classify the points in its proximity. The method is successfully applied to problems with a large amount of instances and high dimension like intrusion detection and microarray gene expression.

Enhancing Clustering in Wireless Sensor Networks with Energy Heterogeneity

While wireless sensor networks (WSN) are increasingly equipped to handle more complex functions, in-network processing still requires the battery-powered sensors to judiciously use their constrained energy so as to prolong the elective network life time. There are a few protocols using sensor clusters to coordinate the energy consumption in a WSN, but how to deal with energy heterogeneity remains a research question. The authors propose a modified clustering algorithm with a three-tier energy setting, where energy consumption among sensor nodes is adaptive to their energy levels. A theoretical analysis shows that the proposed modifications result in an extended network stability period. Simulation has been conducted to evaluate the new clustering algorithm against some existing algorithms under different energy heterogeneity settings, and favourable results are obtained especially when the energy levels are significantly imbalanced.

Forward and reverse modeling of electron beam welding process using radial basis function neural networks

An attempt has been made in the present study to model input-output relationships of an electron beam welding process in both forward as well as reverse directions using radial basis function neural networks. The performance of this network is dependent on its architecture significantly, which, in turn, depends on the number of hidden neurons, as the number of input nodes and that of output neurons can be decided beforehand for modeling a particular process. Input-output data can be clustered based on their similarity among them. The number of hidden neurons of this network is generally kept equal to that of clusters made by the data-set. Two popular fuzzy clustering algorithms, namely fuzzy C-means and entropy-based fuzzy clustering have been used for grouping the data into some clusters. As both these algorithms have inherent limitations, a modified clustering algorithm has been proposed by eliminating their demerits and combining their advantages. Radial basis function neural network developed using the proposed clustering algorithm is found to perform better than that designed based on the above two well-known clustering algorithms.

Adaptive spatial information-theoretic clustering for image segmentation

The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.

Mass gap in the 2D O(3) nonlinear sigma model with aθ=πterm

By analytic continuation to real {theta} of data obtained from numerical simulation at imaginary {theta} we study the Haldane conjecture and show that the O(3) nonlinear sigma model with a {theta} term in two dimensions becomes massless at {theta}=3.10(5). A modified cluster algorithm has been introduced to simulate the model with imaginary {theta}. Two different definitions of the topological charge on the lattice have been used; one of them needs renormalization to match the continuum operator. Our work also offers a successful test for numerical methods based on analytic continuation.

Multi-face detection based on downsampling and modified subtractive clustering for color images

This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.

Application of clustering algorithm to optimizing tool handling management for FMS

This study implements clustering algorithm on the tool-handling problem in the flexible manufacturing system and compares its performance with traditional mixed integer programming approach. The results of simulation show that the percentage of error to optimum applying clustering algorithm to three-machine cases is not worse than that of twomachine cases. Meanwhile, CPU time ratio of MIP to clustering algorithm for three-machine is not dominated by two-machine cases.We conclude that by modifying clustering algorithm appropriately then an appropriately modified clustering algorithm could be implemented on a tool-handling problem with larger scale and still hold its efficiency.

Cluster analysis by the K-means algorithm and simulated annealing

A modified clustering algorithm based on a combination of simulated annealing with the K - means algorithm (SAKMC)is put forward and applied to chemometrics research. An empirical stopping criterion and a perturbation method which is more feasible than those proposed in the literature are suggested. The algorithm is tested on simulated data, and then used for the classification of samples of Chinese medicine calculus bovis or bezoar and samples of Chinese tea. The algorithm which guaranteed in obtaining a global optimum with shorter time compared favorably with traditional cluster analysis based on simulated annealing and the K-means algorithm.

Feature Extraction Using Problem Localization

Feature extraction is considered as a mean-quare estimation of the Bayes risk vector. The problem is simplified by partitioning the distribution space into local subregions and performing a linear estimation in each subregion. A modified clustering algorithm is used to fimd the partitioning which minimizes the mean-square error.