Modified K-Means Clustering Algorithm Research Articles

A New Hybrid Routing Protocol Using a Modified K-Means Clustering Algorithm and Continuous Hopfield Network for VANET

Vehicular Ad-hoc Networks (VANET) offer several user applications for passengers and drivers, as well as security and internet access applications. To ensure efficient data transmission between vehicles, a reliable routing protocol is considered a significant challenge. This paper suggests a new clustering-based routing protocol combining a modified K-Means algorithm with Continuous Hopfield Network and Maximum Stable Set Problem (KMRP) for VANET. In this way, the basic input parameters of the K-Means algorithm, such as the number of clusters and the initial cluster heads, will not be selected randomly, but using Maximum Stable Set Problem and Continuous Hopfield Network. Then the assignment of vehicles to clusters will be carried out according to Link Reliability Model as a metric that replaces the distance parameter in the K-Means algorithm. Finally, the cluster head is selected by weight function according to the amount of free buffer space, the speed, and the node degree. The simulation results have proved that the designed protocol performs better in a highway vehicular environment, compared to the most recent schemes designed for the same objective. In fact, KMRP reduces traffic congestion, and thus provides a significant increase in Throughput. In addition, KMRP decreases the transmission delay and guarantees the stability of the clusters in high density and mobility, which acts better in terms of the Packet Delivery Ratio.

Initial Seed Selection for Mixed Data Using Modified K-means Clustering Algorithm

Data sets to which clustering is applied may be homogeneous (numerical or categorical) or heterogeneous (numerical and categorical) in nature. Handling homogeneous data is easier than heterogeneous data. We propose a novel technique for identifying initial seeds for heterogeneous data clustering, through the introduction of a unique distance measure where the distance of the numerical attributes is scaled such that it is comparable to that of categorical attributes. The proposed initial seed selection algorithm ensures selection of initial seed points from different clusters of the clustering solution which are then given as input to the modified K-means clustering algorithm along with the data set. This technique is independent of any user-defined parameter and thus can be easily applied to clusterable data sets with mixed attributes. We have also modified the K-means clustering algorithm to handle mixed attributes by incorporating our novel distance measure to handle numerical data and assigned the value one or zero when categorical data is dissimilar or similar. Finally, a comparison has been made with existing algorithms to bring out the significance of our approach. We also perform a statistical test to evaluate the statistical significance of our proposed technique.

Evaluation of H- and G-indices of Scientific Authors using Modified K-Means Clustering Algorithm

In this paper I proposed modified K-means algorithm as the means to assess scientific authors performance by using their h,g-indices values. K-means suffers from poor computational scaling and efficiency as the number of clusters has to be supplied by the user. In this work, I introduce a modification of K-means algorithm that efficiently searches the data to cluster points by compute the sum of squares within each cluster which makes the program to select the most promising subset of classes for clustering. The proposed algorithm was tested on IRIS and ZOO data sets as well as on our local dataset comprising of h- and g-indices, which are the prominent markers for scientific excellence of authors publishing papers in various national and international journals. Results from analyses reveal that the modified k-means algorithm is much faster and outperforms the conventional algorithm in terms of clustering performance, measured by the data discrepancy factor. Index Terms—Modified k-means clustering, h-index, g- index.

Feasible Initial Population with Genetic Diversity for a Population-Based Algorithm Applied to the Vehicle Routing Problem with Time Windows

A stochastic algorithm for obtaining feasible initial populations to the Vehicle Routing Problem with Time Windows is presented. The theoretical formulation for the Vehicle Routing Problem with Time Windows is explained. The proposed method is primarily divided into a clustering algorithm and a two-phase algorithm. The first step is the application of a modifiedk-means clustering algorithm which is proposed in this paper. The two-phase algorithm evaluates a partial solution to transform it into a feasible individual. The two-phase algorithm consists of a hybridization of four kinds of insertions which interact randomly to obtain feasible individuals. It has been proven that different kinds of insertions impact the diversity among individuals in initial populations, which is crucial for population-based algorithm behavior. A modification to the Hamming distance method is applied to the populations generated for the Vehicle Routing Problem with Time Windows to evaluate their diversity. Experimental tests were performed based on the Solomon benchmarking. Experimental results show that the proposed method facilitates generation of highly diverse populations, which vary according to the type and distribution of the instances.

Evaluation of h- and g-indices of Scientific Authors using Modified K-Means Clustering Algorithm

<p>In this paper we proposed modified K-means algorithm to assess scientific authors performance by using their h,g-indices values. K-means suffers from poor computational scaling and efficiency as the number of clusters has to be supplied by the user. Hence, in this work, we introduce a modification of K-means algorithm that efficiently searches the data to cluster points by compute the sum of squares within each cluster which makes the program to select the most promising subset of classes for clustering. The proposed algorithm was tested on IRIS and ZOO data sets as well as on our local dataset comprising of h- and g-indices, which are the prominent markers for scientific excellence of authors publishing papers in various national and international journals. Results from analysis reveal that the modified k-means algorithm is much faster and outperforms the conventional algorithm in terms of clustering performance, measured by the data discrepancy factor.</p>

Investigation of Validity Metrics for Modified K-Means Clustering Algorithm

Investigation of Validity Metrics for Modified K-Means Clustering Algorithm

A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.

Reasoning about system-level failure behavior from large sets of function-based simulations

AbstractThis paper presents the use of data clustering methods applied to the analysis results of a design-stage, functional failure reasoning tool. A system simulation using qualitative descriptions of component behaviors and a functional reasoning tool are used to identify the functional impact of a large set of potential single and multiple fault scenarios. The impact of each scenario is collected as the set of categorical function “health” states for each component-level function in the system. This data represents the space of potential system states. The clustering and statistical tools presented in this paper are used to identify patterns in this system state space. These patterns reflect the underlying emergent failure behavior of the system. Specifically, two data analysis tools are presented and compared. First, a modifiedk-means clustering algorithm is used with a distance metric of functional effect similarity. Second, a statistical approach known as latent class analysis is used to find an underlying probability model of potential system failure states. These tools are used to reason about how the system responds to complex fault scenarios and assists in identifying potential design changes for fault mitigation. As computational power increases, the ability to reason with large sets of data becomes as critical as the analysis methods used to collect that data. The goal of this work is to provide complex system designers with a means of using early design simulation data to identify and mitigate potential emergent failure behavior.

Application of Modified K-Means Clustering Algorithm for Satellite Image Segmentation based on Color Information

The Image segmentation is the process of clustering or partitioning the image into number of sub images based on any one characteristics of the image such as colour, intensity or texture. The segmentation is the one of the middle level important process in the image analysis. The number of segmentation algorithms has been developed for various applications. In the field of satellite image processing, the segmentation is one of the vital step for gathering huge amount of information from the satellite images. The basic k-means clustering algorithm is simple and fast. The main problem associated with this clustering is not producing the same result for every run because the resulting clusters depends on the initial random assignments. In this paper, a modified k-means clustering algorithm is proposed for the effective segmentation of the satellite images. This proposed method always produces the same result for every run. The experimental results proved that the improved k-means algorithm is an efficient and effective method for the satellite image segmentation for the exact and accurate segmentation of satellite images. Keywords: image segmentation; satellite image; kmeans clustering; centroid; fuzzy logic