Browsing by Subject "Clustering algorithms"

Now showing 1 - 13 of 13

Open Access
CiSE: a circular spring embedder layout algorithm
(Institute of Electrical and Electronics Engineers, 2013) Dogrusoz, U.; Belviranli, M. E.; Dilek, A.
We present a new algorithm for automatic layout of clustered graphs using a circular style. The algorithm tries to determine optimal location and orientation of individual clusters intrinsically within a modified spring embedder. Heuristics such as reversal of the order of nodes in a cluster and swap of neighboring node pairs in the same cluster are employed intermittently to further relax the spring embedder system, resulting in reduced inter-cluster edge crossings. Unlike other algorithms generating circular drawings, our algorithm does not require the quotient graph to be acyclic, nor does it sacrifice the edge crossing number of individual clusters to improve respective positioning of the clusters. Moreover, it reduces the total area required by a cluster by using the space inside the associated circle. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory, surpassing previous algorithms. The algorithm has also been successfully implemented and made publicly available as part of a compound and clustered graph editing and layout tool named Chisio. © 1995-2012 IEEE.
Open Access
Computational analysis of complicated metamaterial structures using MLFMA and nested preconditioners
(IEEE, 2007-11) Ergül, Özgür; Malas, Tahir; Yavuz, Ç; Ünal, Alper; Gürel, Levent
We consider accurate solution of scattering problems involving complicated metamaterial (MM) structures consisting of thin wires and split-ring resonators. The scattering problems are formulated by the electric-field integral equation (EFIE) discretized with the Rao-Wilton- Glisson basis functions defined on planar triangles. The resulting dense matrix equations are solved iteratively, where the matrix-vector multiplications that are required by the iterative solvers are accelerated with the multilevel fast multipole algorithm (MLFMA). Since EFIE usually produces matrix equations that are ill-conditioned and difficult to solve iteratively, we employ nested preconditioners to achieve rapid convergence of the iterative solutions. To further accelerate the simulations, we parallelize our algorithm and perform the solutions on a cluster of personal computers. This way, we are able to solve problems of MMs involving thousands of unit cells.
Open Access
EHPBS: Energy harvesting prediction based scheduling in wireless sensor networks
(IEEE, 2013) Akgun, B.; Aykın, Irmak
The clustering algorithms designed for traditional sensor networks have been adapted for energy harvesting sensor networks (EHWSN). However, in these algorithms, the intra-cluster MAC protocols to be used were either not defined at all or they were TDMA based. These TDMA based MAC protocols are not specified except for the fact that cluster heads assign time slots to their members in a random manner. In this paper, we will modify this TDMA based scheduling as follows: members will request a time slot depending on their energy prediction and the cluster heads will assign these slots to members. This method will increase the network lifetime. The proof will be given with simulations. © 2013 IEEE.
Open Access
Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization
(IEEE, 2010-08) Arı, Çağlar; Aksoy, Selim
We present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows independent updating of individual parameters while retaining the validity of the matrix. The second solution involves an optimization formulation for finding correspondences between different parameter orderings of candidate solutions. The effectiveness of the proposed solutions are demonstrated on a novel clustering algorithm based on particle swarm optimization for the estimation of Gaussian mixture models. © 2010 IEEE.
Open Access
Motion based clustering of model animations using PCA
(IEEE, 2009) Köse, Kıvanç; Çetin, A. Enis
In the last few years, there is great increase in capture and representation of real 3-Dimensonal scenes using 3D animation models. The 3D signals are then compressed, transmitted to the client side and reconstructed for the user view. Each step mentioned here opened a new subject in the field of signal processing. While processing these models, using the model as a whole is not the best approach. Therefore clustering the model vertices became a very common method. For example, it is very common to use motion based clustering in animation compression. In this paper a new dynamic model clustering algorithm is proposed. Animation vertices are first put through PCA and partitioned into their eigenvalues and eigenvectors. The eigenvectors found using the proposed method can be called eigentrajectories. Then the dot product of the these eigentrajectories with the trajectories of the animation vertice are found. These coefficients are used to cluster the animation model. The results and the comparisons with a similar approach show that the proposed algorithm is successful.
Open Access
Multirelational k-anonymity
(IEEE, 2007-04) Nergiz, M. Ercan; Clifton, C.; Nergiz, A. Erhan
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. Much research has been done to modify a single table dataset to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy, or overly reduce the utility of the data, in a multiple relation setting. A new clustering algorithm is proposed to achieve multirelational anonymity. © 2007 IEEE.
Open Access
Multirelational k-anonymity
(2009) Nergiz, M.E.; Clifton, C.; Nergiz, A.E.
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy or overly reduce the utility of the data in a multiple relation setting. We also propose two new clustering algorithms to achieve multirelational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency. © 2006 IEEE.
Open Access
A new approach to search result clustering and labeling
(Springer, Berlin, Heidelberg, 2011) Türel, Anıl; Can, Fazlı
Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.
Open Access
Online balancing two independent criteria
(Springer, 2008-10) Tse, Savio S.H.
We study the online bicriteria load balancing problem in this paper. We choose a system of distributed homogeneous file servers located in a cluster as the scenario and propose two online approximate algorithms for balancing their loads and required storage spaces. We first revisit the best existing solution for document placement, and rewrite it in our first algorithm by imposing some flexibilities. The second algorithm bounds the load and storage space of each server by less than three times of their trivial lower bounds, respectively; and more importantly, for each server, the value of at least one parameter is far from its worst case. The time complexities for both algorithm are O(logM). © 2008 Springer Berlin Heidelberg.
Open Access
Parallel pruning for k-means clustering on shared memory architectures
(Springer Verlag, 2001) Gürsoy, Attila; Cengiz, Ilker
We have developed and evaluated two parallelization schemes for a tree-based k-means clustering method on shared memory machines. One scheme is to partition the pattern space across processors. We have determined that spatial decomposition of patterns outperforms random decomposition even though random decomposition has almost no load imbalance problem. The other scheme is the parallel traverse of the search tree. This approach solves the load imbalance problem and performs slightly better than the spatial decomposition, but the efficiency is reduced due to thread synchronizations. In both cases, parallel treebased k-means clustering is significantly faster than the direct parallel k-means. © Springer-Verlag Berlin Heidelberg 2001.
Open Access
Scene classification using bag-of-regions representations
(IEEE, 2007-06) Gökalp, Demir; Aksoy, Selim
This paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a "bag of individual regions" representation where each region is regarded separately, and a "bag of region pairs" representation where regions with particular spatial relationships are considered, together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly out-perform a baseline global feature-based approach. © 2007 IEEE.
Open Access
Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization
(IEEE, 2010) Arı, Çağlar; Aksoy, Selim
Gaussian mixture models (GMM) are widely used for un-supervised classification applications in remote sensing. Expectation-Maximization (EM) is the standard algorithm employed to estimate the parameters of these models. However, such iterative optimization methods can easily get trapped into local maxima. Researchers use population-based stochastic search algorithms to obtain better estimates. We present a novel particle swarm optimization-based algorithm for maximum likelihood estimation of Gaussian mixture models. The proposed approach provides solutions for important problems in effective application of population-based algorithms to the clustering problem. We present a new parametrization for arbitrary covariance matrices that allows independent updating of individual parameters during the search process. We also describe an optimization formulation for identifying the correspondence relations between different parameter orderings of candidate solutions. Experiments on a hyperspectral image show better clustering results compared to the commonly used EM algorithm for estimating GMMs. © 2010 IEEE.
Open Access
A web-site-based partitioning technique for reducing preprocessing overhead of parallel PageRank computation
(Springer, Berlin, Heidelberg, 2007) Cevahir, Ali; Aykanat, Cevdet; Turk, Ata; Cambazoğlu, B. Barla
A power method formulation, which efficiently handles the problem of dangling pages, is investigated for parallelization of PageRank computation. Hypergraph-partitioning-based sparse matrix partitioning methods can be successfully used for efficient parallelization. However, the preprocessing overhead due to hypergraph partitioning, which must be repeated often due to the evolving nature of the Web, is quite significant compared to the duration of the PageRank computation. To alleviate this problem, we utilize the information that sites form a natural clustering on pages to propose a site-based hypergraph-partitioning technique, which does not degrade the quality of the parallelization. We also propose an efficient parallelization scheme for matrix-vector multiplies in order to avoid possible communication due to the pages without in-links. Experimental results on realistic datasets validate the effectiveness of the proposed models. © Springer-Verlag Berlin Heidelberg 2007.