42,253 research outputs found
Cluster-Based Load Balancing Algorithms for Grids
E-science applications may require huge amounts of data and high processing
power where grid infrastructures are very suitable for meeting these
requirements. The load distribution in a grid may vary leading to the
bottlenecks and overloaded sites. We describe a hierarchical dynamic load
balancing protocol for Grids. The Grid consists of clusters and each cluster is
represented by a coordinator. Each coordinator first attempts to balance the
load in its cluster and if this fails, communicates with the other coordinators
to perform transfer or reception of load. This process is repeated
periodically. We analyze the correctness, performance and scalability of the
proposed protocol and show from the simulation results that our algorithm
balances the load by decreasing the number of high loaded nodes in a grid
environment.Comment: 17 pages, 11 figures; International Journal of Computer Networks,
volume3, number 5, 201
Enhanced Cluster Based Routing Protocol for MANETS
Mobile ad-hoc networks (MANETs) are a set of self organized wireless mobile
nodes that works without any predefined infrastructure. For routing data in
MANETs, the routing protocols relay on mobile wireless nodes. In general, any
routing protocol performance suffers i) with resource constraints and ii) due
to the mobility of the nodes. Due to existing routing challenges in MANETs
clustering based protocols suffers frequently with cluster head failure
problem, which degrades the cluster stability. This paper proposes, Enhanced
CBRP, a schema to improve the cluster stability and in-turn improves the
performance of traditional cluster based routing protocol (CBRP), by electing
better cluster head using weighted clustering algorithm and considering some
crucial routing challenges. Moreover, proposed protocol suggests a secondary
cluster head for each cluster, to increase the stability of the cluster and
implicitly the network infrastructure in case of sudden failure of cluster
head.Comment: 6 page
Measuring Visual Complexity of Cluster-Based Visualizations
Handling visual complexity is a challenging problem in visualization owing to
the subjectiveness of its definition and the difficulty in devising
generalizable quantitative metrics. In this paper we address this challenge by
measuring the visual complexity of two common forms of cluster-based
visualizations: scatter plots and parallel coordinatess. We conceptualize
visual complexity as a form of visual uncertainty, which is a measure of the
degree of difficulty for humans to interpret a visual representation correctly.
We propose an algorithm for estimating visual complexity for the aforementioned
visualizations using Allen's interval algebra. We first establish a set of
primitive 2-cluster cases in scatter plots and another set for parallel
coordinatess based on symmetric isomorphism. We confirm that both are the
minimal sets and verify the correctness of their members computationally. We
score the uncertainty of each primitive case based on its topological
properties, including the existence of overlapping regions, splitting regions
and meeting points or edges. We compare a few optional scoring schemes against
a set of subjective scores by humans, and identify the one that is the most
consistent with the subjective scores. Finally, we extend the 2-cluster measure
to k-cluster measure as a general purpose estimator of visual complexity for
these two forms of cluster-based visualization
Cluster-based analogue ensembles for hindcasting with multistations
The Analogue Ensemble (AnEn) method enables the reconstruction of meteorological observations or deterministic predictions for a certain variable and station by using data from the same station or from other nearby stations. However, depending on the dimension and granularity of the historical datasets used for the reconstruction, this method may be computationally very demanding even if parallelization is used. In this work, the classical AnEn method is modified so that analogues are determined using K-means clustering. The proposed combined approach allows the use of several predictors in a dependent or independent way. As a result of the flexibility and adaptability of this new approach, it is necessary to define several parameters and algorithmic options. The effects of the critical parameters and main options were tested on a large dataset from real-world meteorological stations. The results show that adequate monitoring and tuning of the new method
allows for a considerable improvement of the computational performance of the reconstruction task while keeping the accuracy of the results. Compared to the classical AnEn method, the proposed variant is at least 15-times faster when processing is serial. Both approaches benefit from parallel processing, with the K-means variant also being always faster than the classic method under that execution regime (albeit its performance advantage diminishes as more CPU threads are used).info:eu-repo/semantics/publishedVersio
Two-Step Cluster Based Feature Discretization of Naive Bayes for Outlier Detection in Intrinsic Plagiarism Detection
Intrinsic plagiarism detection is the task of analyzing a document with respect to undeclared changes in writing style which treated as outliers. Naive Bayes is often used to outlier detection. However, Naive Bayes has assumption that the values of continuous feature are normally distributed where this condition is strongly violated that caused low classification performance. Discretization of continuous feature can improve the performance of NaĂŻve Bayes. In this study, feature discretization based on Two-Step Cluster for NaĂŻve Bayes has been proposed. The proposed method using tf-idf and query language model as feature creator and False Positive/False Negative (FP/FN) threshold which aims to improve the accuracy and evaluated using PAN PC 2009 dataset. The result indicated that the proposed method with discrete feature outperform the result from continuous feature for all evaluation, such as recall, precision, f-measure and accuracy. The using of FP/FN threshold affects the result as well since it can decrease FP and FN; thus, increase all evaluation
Gene expression reliability estimation through cluster-based analysis
Gene expression is the fundamental control of the structure and functions of the cellular versatility and adaptability of any organisms. The measurement of gene expressions is performed on images generated by optical inspection of microarray devices which allow the simultaneous analysis of thousands of genes. The images produced by these devices are used to calculate the expression levels of mRNA in order to draw diagnostic information related to human disease. The quality measures are mandatory in genes classification and in the decision-making diagnostic. However, microarrays are characterized by imperfections due to sample contaminations, scratches, precipitation or imperfect gridding and spot detection. The automatic and efficient quality measurement of microarray is needed in order to discriminate faulty gene expression levels. In this paper we present a new method for estimate the quality degree and the data's reliability of a microarray analysis. The efficiency of the proposed approach in terms of genes expression classification has been demonstrated through a clustering supervised analysis performed on a set of three different histological samples related to the Lymphoma's cancer diseas
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