set partition by globally optimized cluster seed points abstract a new method is proposed for solving set partition clustering problems the suggested approach is based on the application of global optimization methodology for selecting best cluster seed points the applicability of the method is illustrated by numerical examples

a novel validity index with dynamic cut off for determining true clusters in a multi surveillance environment voluminous data is generated over a period of time data analysis for summarization and conclusion has paved a way for the need of an efficient clusterization clustering an unsupervised way of learning about data aims at defining clusters validation of clusters formed indicates the trueness of the clusters in this paper a novel validation technique with dynamic termination of clustering process has been proposed to obtain true clusters in the validation process the validity index is based on both global cluster proximity relationship and local proximity relationship the validity index is computed for validating the available clusters using within cluster sum of squares between cluster sum of squares total sum of squares intra cluster distances and inter cluster distances the ratio between two consecutive validity indices is the extent of variation which specifies the cut off point cut off terminates the clustering process dynamically indicating the number of clusters and validates the obtained clusters the proposed method is tested on several real and synthetic data sets comparisons with the existing methods demonstrate the efficiency of the proposed method in detecting true clusters a novel validation technique has been proposed to obtain true clusters the method dynamically terminates the clustering at true conception of clusters global and local proximity relationship of clusters are considered for validation

multipath clustering and cluster tracking for geometry based stochastic channel modeling this paper presents a clustering and tracking method that exploits the geometry of the scattering points sps obtained from the measurement based ray tracer the multipath components mpcs were categorized into clusters by applying the kpowermeans kpm framework to those sps the clusters were tracked by comparing the cluster centroid sps of the adjacent snapshots the clusters were estimated based on the indoor environment geometry at 11 ghz and their physical mechanisms were interpreted the complexity and performance of this method was assessed and compared with that of conventional kpm by comparing the number of floating point operations flops and the channel eigenvalues obtained from the reconstructed channel matrices which were calculated by superposing the mpcs randomly generated from intracluster parameters the verification of this method showed that most clusters were estimated and tracked according to the physical location of the scatterers in the environment with acceptable error moreover the eigenvalues reconstructed from the proposed method were closer to the measured ones with less number of flops which indicates both accuracy and complexity improvement the results also imply that multiple input multiple output performance is highly dependent on the radio propagation channel therefore it is imperative that clusters in the channel be determined accurately

density based geodesic distance for identifying the noisy and nonlinear clusters we propose a density based geodesic distance that resists noise for clustering the proposed distance is calculated based on the neighborhood graph for nonlinearity simulation and real case data demonstrate the accuracy of the proposed distance clustering analysis can facilitate the extraction of implicit patterns in a dataset and elicit its natural groupings without requiring prior classification information for superior clustering analysis results a number of distance measures have been proposed recently geodesic distance has been widely applied to clustering algorithms for nonlinear groupings however geodesic distance is sensitive to noise and hence geodesic distance based clustering may fail to discover nonlinear clusters in the region of the noise in this study we propose a density based geodesic distance that can identify clusters in nonlinear and noisy situations experiments on various simulation and benchmark datasets are conducted to examine the properties of the proposed geodesic distance and to compare its performance with that of existing distance measures the experimental results confirm that a clustering algorithm with the proposed distance measure demonstrated superior performance compared to the competitors this was especially true when the cluster structures in the data were inherently noisy and nonlinearly patterned

simultaneous clustering and model selection algorithm theory and applications while clustering has been well studied in the past decade model selection has drawn much less attention due to the difficulty of the problem in this paper we address both problems in a joint manner by recovering an ideal affinity tensor from an imperfect input by taking into account the relationship of the affinities induced by the cluster structures we are able to significantly improve the affinity input such as repairing those entries corrupted by gross outliers more importantly the recovered ideal affinity tensor also directly indicates the number of clusters and their membership thus solving the model selection and clustering jointly to enforce the requisite global consistency in the affinities demanded by the cluster structure we impose a number of constraints specifically among others the tensor should be low rank and sparse and it should obey what we call the rank 1 sum constraint to solve this highly non smooth and non convex problem we exploit the mathematical structures and express the original problem in an equivalent form amenable for numerical optimization and convergence analysis to scale to large problem sizes we also propose an alternative formulation so that those problems can be efficiently solved via stochastic optimization in an online fashion we evaluate our algorithm with different applications to demonstrate its superiority and show it can adapt to a large variety of settings

solution of voter model dynamics on annealed small world networks an analytical study of the behavior of the voter model on the small world topology is performed in order to solve the equations for the dynamics we consider an annealed version of the watts strogatz ws network where long range connections are randomly chosen at each time step the resulting dynamics is as rich as on the original ws network a temporal scale tau separates a quasistationary disordered state with coexisting domains from a fully ordered frozen configuration tau is proportional to the number of nodes in the network so that the system remains asymptotically disordered in the thermodynamic limit

feature selection using structural similarity a new method of feature selection is developed based on structural similarity the topological neighborhood information about pairs of objects or patterns to partition s is taken into consideration while computing a measure of structural similarity this is termed proximity and is defined in terms of membership values multi objective evolutionary optimization is employed to arrive at a consensus solution in terms of the contradictory criteria pair involving fuzzy proximity and feature set cardinality results for real and synthetic datasets of low medium and high dimensionality show that the method led to a correct selection of the reduced feature subset comparative study is also provided and quantified in terms of accuracy of classification and clustering validity indices

cluster based population initialization for differential evolution frameworks this article proposes a procedure to perform an intelligent initialization for population based algorithms the proposed pre processing procedure namely cluster based population initialization cbpi consists of three consecutive stages at the first stage the individuals belonging to a randomly sampled population undergo two subsequent local search algorithms i e a simple local search that performs moves along the axes and rosenbrock algorithm at the second stage the solutions processed by the two local searches undergo the k means clustering algorithm and are grouped into sets on the basis of their euclidean distance at the third stage the best individuals belonging to each cluster are saved into the initial population of a generic optimization algorithm if the population has not been yet filled the other individuals of the population are sampled within the clusters by using a fitness based probabilistic criterion this three stage procedure implicitly performs an initial screening of the problem features in order to roughly estimate the most interesting regions of the decision space the proposed cbpi has been tested on multiple classical and modern differential evolution variants on a wide array of test problems and dimensionality values as well as on a real world problem the proposed intelligent sampling appears to have a significant impact on the algorithmic functioning as it consistently enhances the performance of the algorithms with which it is integrated

modeling simulating and parameter fitting of biochemical kinetic experiments in many chemical and biological applications systems of differential equations containing unknown parameters are used to explain empirical observations and experimental data the differential equations are typically nonlinear and difficult to analyze requiring numerical methods to approximate the solutions compounding this difficulty are the unknown parameters in the differential equation system which must be given specific numerical values in order for simulations to be run estrogen receptor protein dimerization is used as an example to demonstrate model construction reduction simulation and parameter estimation mathematical computational and statistical methods are applied to empirical data to deduce kinetic parameter estimates and guide decisions regarding future experiments and modeling the process demonstrated serves as a pedagogical example of quantitative methods being used to extract parameter values from biochemical data models

shadowed c means integrating fuzzy and rough clustering a new method of partitive clustering is developed in the framework of shadowed sets the core and exclusion regions of the generated shadowed partitions result in a reduction in computations as compared to conventional fuzzy clustering unlike rough clustering here the choice of threshold parameter is fully automated the number of clusters is optimized in terms of various validity indices it is observed that shadowed clustering can efficiently handle overlapping among clusters as well as model uncertainty in class boundaries the algorithm is robust in the presence of outliers a comparative study is made with related partitive approaches experimental results on synthetic as well as real data sets demonstrate the superiority of the proposed approach

estimation of the centre of rotation a methodological contribution the location of the centre of rotation of human joints that can be modelled as a spherical hinge can be estimated using kinematics information about the two adjacent bony segments involved recorded while the subject makes them move one relative to the other functional method in order to solve the relevant analytical problem several algorithms have been proposed most recently two methods one based on a spherical best fit approach and another based on the reuleaux construction have been presented as being different and submitted to comparative evaluation this paper modifies the second method taking all information in the data set into account and shows that having done this the two methods coincide analytically

a novel clustering method based on hybrid k nearest neighbor graph abstract most of the existing clustering methods have difficulty in processing complex nonlinear data sets to remedy this deficiency in this paper a novel data model termed hybrid k nearest neighbor hknn graph which combines the advantages of mutual k nearest neighbor graph and k nearest neighbor graph is proposed to represent the nonlinear data sets moreover a clustering method based on the hknn graph chknn is proposed the chknn first generates several tight and small subclusters then merges these subclusters by exploiting the connectivity among them in order to select the optimal parameters for chknn we further propose an internal validity index termed k nearest neighbor index knni which can also be used to evaluate the validity of nonlinear clustering results by varying a control parameter experimental results on synthetic and real world data sets as well as that on the video clustering have demonstrated the significant improvement on performance over existing nonlinear clustering methods and internal validity indices

cpcq contrast pattern based clustering quality index for categorical data clustering validation is concerned with assessing the quality of clustering solutions since clustering is unsupervised and highly explorative clustering validation has been an important and long standing research problem existing validity measures including entropy based and distance based indices have significant shortcomings indeed for many datasets from the uci repository they fail to recognize that the expert determined classes are the best clusters and they frequently give preference to clusterings with larger number of clusters their weakness reflects their inability to accurately capture intra cluster coherence and inter cluster separation this paper proposes a novel contrast pattern based clustering quality index cpcq for categorical data by utilizing the quality and diversity of the contrast patterns which contrast the clusters in given clusterings high quality contrast patterns can serve to characterize the clusters and discriminate one cluster against the others the cpcq index is based on the rationale that a high quality clustering should have many diversified high quality contrast patterns among its clusters the quality of individual contrast patterns is defined in terms of their length support and the length of their corresponding closed pattern the quality measure concerning many diversified contrast patterns is defined in terms of the quality and diversity of some selected groups of contrast patterns with minimal overlap among contrast patterns and groups in terms of items and matching transactions experiments show that the cpcq index 1 does not require a user to provide a distance function 2 does not give inappropriate preference to larger number of clusters 3 can recognize that expert determined classes are the best clusters for many datasets from the uci repository

the heterogeneous p median problem for categorization based clustering the p median offers an alternative to centroid based clustering algorithms for identifying unobserved categories however existing p median formulations typically require data aggregation into a single proximity matrix resulting in masked respondent heterogeneity a proposed three way formulation of the p median problem explicitly considers heterogeneity by identifying groups of individual respondents that perceive similar category structures three proposed heuristics for the heterogeneous p median hpm are developed and then illustrated in a consumer psychology context using a sample of undergraduate students who performed a sorting task of major u s retailers as well as a through monte carlo analysis

silhouettes a graphical aid to the interpretation and validation of cluster analysis a new graphical display is proposed for partitioning techniques each cluster is represented by a so called silhouette which is based on the comparison of its tightness and separation this silhouette shows which objects lie well within their cluster and which ones are merely somewhere in between clusters the entire clustering is displayed by combining the silhouettes into a single plot allowing an appreciation of the relative quality of the clusters and an overview of the data configuration the average silhouette width provides an evaluation of clustering validity and might be used to select an appropriate number of clusters

a survey of formal methods for determining the centre of rotation of ball joints abstract the determination of an accurate centre of rotation cor from segment marker positions is of interest across a wide range of applications but particularly for clinical gait analysis and for estimating the hip joint centre during surgical intervention of the knee for limb alignment purposes for the first time in this survey of formal methods we classify analyse and compare different methods geometric algebraic bias compensated algebraic and pratt sphere fit methods as well as the centre transformation technique the holzreiter approach the helical pivot technique the schwartz transformation techniques the minimal amplitude point method and the stoddart approach for the determination of spherical joint centres from marker position data in addition we propose a new method the symmetrical cor estimation or score in which the coordinates of the joint centre must only remain constant relative to each segment thus not requiring the assumption that one segment should remain at rest for each method 1000 cor estimations were analysed with the application of isotropic independent and identically distributed gaussian noise standard deviation 0 1 cm to each of the marker positions to all markers on the segment simultaneously and the two in combination for the test conditions used here most techniques were capable of determining the cor to within 0 3 cm as long as the spherical range of motion rom of the joint was 45 or more under the most stringent conditions tested however the score was capable of best determining the cor to within approximately 1 2 mm with a rom of 20 the correct selection and application of these methodologies should help improve the accuracy of surgical navigation and clinical kinematic measurement

on correlation between two fuzzy sets in this paper we investigated the correlation between two fuzzy sets defined on the same universal support it is shown that spearman s rank correlation coefficient can be applied if the members of the supports are ranked according to the fuzzy membership values of each set next a membership value based fuzzy correlation measure is proposed and its properties are explained the proposed measure is compared with an earlier definition 4

multiway spectral clustering with out of sample extensions through weighted kernel pca a new formulation for multiway spectral clustering is proposed this method corresponds to a weighted kernel principal component analysis pca approach based on primal dual least squares support vector machine ls svm formulations the formulation allows the extension to out of sample points in this way the proposed clustering model can be trained validated and tested the clustering information is contained on the eigendecomposition of a modified similarity matrix derived from the data this eigenvalue problem corresponds to the dual solution of a primal optimization problem formulated in a high dimensional feature space a model selection criterion called the balanced line fit blf is also proposed this criterion is based on the out of sample extension and exploits the structure of the eigenvectors and the corresponding projections when the clusters are well formed the blf criterion can be used to obtain clustering parameters in a learning framework experimental results with difficult toy problems and image segmentation show improved performance in terms of generalization to new samples and computation times

reliability based fuzzy clustering ensemble abstract in the clustering ensemble the quality of base clusterings influences the consensus clustering although some researches have been devoted to weighting the base clustering fuzzy cluster level weighting has been ignored more specifically they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble in this paper we propose a new fuzzy clustering ensemble framework without access to the features of data objects based on fuzzy cluster level weighting the reliability of each fuzzy cluster is computed based on estimation of its unreliability and is considered as its weight in the ensemble the unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion in our framework the final clustering is produced by two types of consensus functions 1 a reliability based weighted fuzzy co association matrix is constructed from the base clusterings and then a single traditional clustering such as hierarchical agglomerative clustering or k means is applied over the matrix to produce the final clustering 2 a new graph based fuzzy consensuses function the graph based consensus function has linear time complexity in the number of data objects experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state of the art methods in terms of evaluation criteria and clustering robustness

automatic aspect discrimination in data clustering the attributes describing a data set may often be arranged in meaningful subsets each of which corresponds to a different aspect of the data an unsupervised algorithm scad that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature however scad may fail and halt given certain conditions to fix this problem its steps are modified and then reordered to reduce the number of parameters required to be set by the user in this paper we prove that each step of the resulting algorithm named ascad globally minimizes its cost function with respect to the argument being optimized the asymptotic analysis of ascad leads to a time complexity which is the same as that of fuzzy c means a hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described the proposed method is assessed over several artificial and real data sets

multi manifold matrix decomposition for data co clustering we propose a novel multi manifold matrix decomposition for co clustering m3dc algorithm that considers the geometric structures of both the sample manifold and the feature manifold simultaneously specifically multiple candidate manifolds are constructed separately to take local invariance into account then we employ multi manifold learning to approximate the optimal intrinsic manifold which better reflects the local geometrical structure by linearly combining these candidate manifolds in m3dc the candidate manifolds are obtained using various manifold based dimensionality reduction methods these methods are based on different rationales and use different metrics for data distances experimental results on several real data sets demonstrate the effectiveness of our proposed m3dc highlightswe consider the geometric structures of both sample and feature manifolds to reduces the complexity we use two low dimensional intermediate matrices we employ multi manifold learning to approximate the intrinsic manifold the intrinsic manifold is constructed by linearly combining multiple manifolds the candidate manifolds are constructed using six dimensionality reduction methods

positional and confidence voting based consensus functions for fuzzy cluster ensembles consensus clustering i e the task of combining the outcomes of several clustering systems into a single partition has lately attracted the attention of researchers in the unsupervised classification field as it allows the creation of clustering committees that can be applied with multiple interesting purposes such as knowledge reuse or distributed clustering however little attention has been paid to the development of algorithms known as consensus functions especially designed for consolidating the outcomes of multiple fuzzy or soft clustering systems into a single fuzzy partition despite the fact that fuzzy clustering is far more informative than its crisp counterpart as it provides information regarding the degree of association between objects and clusters that can be helpful for deriving richer descriptive data models for this reason this paper presents a set of fuzzy consensus functions capable of creating soft consensus partitions by fusing a collection of fuzzy clusterings our proposals base clustering combination on a cluster disambiguation process followed by the application of positional and confidence voting techniques the modular design of these algorithms makes it possible to sequence their constituting steps in different manners which allows to derive versions of the proposed consensus functions optimized from a computational standpoint the proposed consensus functions have been evaluated in terms of the quality of the consensus partitions they deliver and in terms of their running time on multiple benchmark data sets a comparison against several representative state of the art consensus functions reveals that our proposals constitute an appealing alternative for conducting fuzzy consensus clustering as they are capable of yielding high quality consensus partitions at a low computational cost

characterizing the functional connectivity diversity of the insula cortex subregions diversity curves and behavior the connectivity of the insula cortex is diverse we present new models to characterize the resting state connectional diversity of the human insula cortex and perform model selection using high quality fmri data from the human connectome project we first attempt to parcellate the insula into distinct subregions using traditional clustering methods but find that the resulting subregions are not homogeneous and that the optimal number of subregions is substantially influenced by data smoothness we then introduce the concept of a diversity curve which we use to continuously parameterize the insula s laplacian eigenmap with respect to streamlines propagated through the eigenmap s gradient field to perform model selection we compare the insula s diversity curve to benchmark diversity curves for i two distinct regions ii a continuum of gradual change and iii an absence of any connectional diversity i e homogenous region of the three benchmarks tested we find that the insula s connectional diversity is most parsimoniously modeled as continuum of gradual change from dorsal posterior to ventral anterior we find that individuals who score high on measures of positive affect self efficacy emotion recognition motor dexterity and gustation show greater diversity within the anterior insula our findings are replicated using data from a second fmri session we conclude that the functional connectivity diversity of the insula can be characterized parsimoniously as a continuum avoiding the vexed task of determining an optimal number of insula subregions and that inter individual variation in this continuum can explain significant variation in behavior

statistical physics of language maps in the usa spatial linguistic surveys often reveal well defined geographical zones where certain linguistic forms are dominant over their alternatives it has been suggested that these patterns may be understood by analogy with coarsening in models of two dimensional physical systems here we investigate this connection by comparing data from the cambridge online survey of world englishes to the behavior of a generalized zero temperature potts model with long range interactions the relative displacements of linguistically similar population centers reveal enhanced east west affinity cluster analysis reveals three distinct linguistic zones we find that when the interaction kernel is made anisotropic by stretching along the east west axis the model can reproduce the three linguistic zones for all interaction parameters tested the model results are consistent with a view held by some linguists that in the usa language use is or has been exchanged or transmitted to a greater extent along the east west axis than the north south

social aggregation as a cooperative game abstract a new approach for the description of phenomena of social aggregation is suggested on the basis of psychological concepts as for instance social norms and cultural coordinates we deduce a general mechanism for social aggregation in which different clusters of individuals can merge according to cooperation among the agents in their turn the agents can cooperate or defect according to the clusters distribution inside the system the fitness of an individual increases with the size of its cluster but decreases with the work the individual had to do in order to join it in order to test the reliability of such a new approach we introduce a couple of simple toy models with the features illustrated above we see from this preliminary study how cooperation is the most convenient strategy only in the presence of very large clusters while on the other hand it is not necessary to have one hundred percent of cooperators for reaching a totally ordered configuration with only one megacluster filling the whole system

ensemble fuzzy clustering using cumulative aggregation on random projections random projection is a popular method for dimensionality reduction due to its simplicity and efficiency in the past few years random projection and fuzzy c means based cluster ensemble approaches have been developed for high dimensional data clustering however they require large amounts of space for storing a big affinity matrix and incur large computation time while clustering in this affinity matrix in this paper we propose a new random projection fuzzy c means based cluster ensemble framework for high dimensional data our framework uses cumulative agreement to aggregate fuzzy partitions fuzzy partitions of random projections are ranked using external and internal cluster validity indices the best partition in the ranked queue is the core or base partition remaining partitions then provide cumulative inputs to the core thus arriving at a consensus best overall partition built from the ensemble experimental results with gaussian mixture datasets and a variety of real datasets demonstrate that our approach outperforms three state of the art methods in terms of accuracy and space time complexity our algorithm runs one to two orders of magnitude faster than other state of the arts algorithms

a model selection approach for clustering a multinomial sequence with non negative factorization we consider a problem of clustering a sequence of multinomial observations by way of a model selection criterion we propose a form of a penalty term for the model selection procedure our approach subsumes both the conventional aic and bic criteria but also extends the conventional criteria in a way that it can be applicable also to a sequence of sparse multinomial observations where even within a same cluster the number of multinomial trials may be different for different observations in addition as a preliminary estimation step to maximum likelihood estimation and more generally to maximum l q estimation we propose to use reduced rank projection in combination with non negative factorization we motivate our approach by showing that our model selection criterion and preliminary estimation step yield consistent estimates under simplifying assumptions we also illustrate our approach through numerical experiments using real and simulated data

evolutionary fuzzy clustering of relational data this paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only relational data and the number of clusters must be automatically estimated from such data a fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic pseudo exhaustive approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data an extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed

multiclass semisupervised learning based upon kernel spectral clustering this paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering ksc as a core model a regularized ksc is formulated to estimate the class memberships of data points in a semisupervised setting using the one versus all strategy while both labeled and unlabeled data points are present in the learning process the propagation of the labels to a large amount of unlabeled data points is achieved by adding the regularization terms to the cost function of the ksc formulation in other words imposing the regularization term enforces certain desired memberships the model is then obtained by solving a linear system in the dual furthermore the optimal embedding dimension is designed for semisupervised clustering this plays a key role when one deals with a large number of clusters

method for determining the optimal number of clusters based on agglomerative hierarchical clustering it is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis from the standpoint of sample geometry two concepts i e the sample clustering dispersion degree and the sample clustering synthesis degree are defined and a new clustering validity index is designed moreover a method for determining the optimal number of clusters based on an agglomerative hierarchical clustering ahc algorithm is proposed the new index and the method can evaluate the clustering results produced by the ahc and determine the optimal number of clusters for multiple types of datasets such as linear manifold annular and convex structures theoretical research and experimental results indicate the validity and good performance of the proposed index and the method

cluster correspondence analysis a method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved in a unified framework a brief review of alternative methods is provided and we show that the proposed method is equivalent to groupals applied to categorical data performance of the methods is appraised by means of a simulation study the results of the joint dimension reduction and clustering methods are compared with the so called tandem approach a sequential analysis of dimension reduction followed by cluster analysis the tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure our simulation study confirms this conjecture moreover the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods

a coherence based approach for the pattern recognition of time series abstract a pattern recognition approach based on the frequency domain measure of squared coherence is a useful approach to identify linearly related groupings of time series over different periods of time it is considered in an application to identify similar patterns of the yearly rates of change in the gross domestic product gdp of twenty two highly developed countries in an econophysics context the approach is also tested in simulation studies using linearly related time series and it is shown to have a very good success rate of correct pattern matching

non hierarchical clustering with masloc abstract masloc is a cluster analysis technique based on the p median model of location theory which permits for each value of p clustering the given objects into p groups each provided with a representative object both an exact and heuristic method are described along with their application range the interesting values for p are then determined along with the more interesting clusters by robustness analysis the paper includes a description of a computer program for masloc and some of the applications it was used for

scale free networks from varying vertex intrinsic fitness a new mechanism leading to scale free networks is proposed in this letter it is shown that in many cases of interest the connectivity power law behavior is neither related to dynamical properties nor to preferential attachment assigning a quenched fitness value x i to every vertex and drawing links among vertices with a probability depending on the fitnesses of the two involved sites gives rise to what we call a good get richer mechanism in which sites with larger fitness are more likely to become hubs i e to be highly connected

fingerprint indexing based on minutiae pairs and convex core point an efficient fingerprint indexing which uses ellipse properties is proposed a new fingerprint indexing is proposed using minutiae pairs and convex core point our fingerprint representation is very robust to distortions and noise the proposed fingerprint features are invariant to rotation and translation fingerprint identification is an important issue for identifying fingerprints and plays a key role in the fingerprint recognition systems however performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms fingerprint indexing is a key strategy in automatic fingerprint identification systems afiss which allows us to reduce the number of candidates the search space and the occurrences of false acceptance in large databases in this paper an efficient indexing algorithm is proposed using minutiae pairs and convex core point which employs k means clustering and candidate list reduction criteria to improve the identification performance our proposal can effectively reduce the search space and number of candidates for fingerprint matching and thus achieves higher efficiency and significantly improves the system retrieval performance experimental results over some of the fingerprint verification competition fvc and the national institute of standards and technology nist databases prove the superiority of the proposed approach against some of the well known indexing algorithms

a clustering ensemble the aim of clustering ensemble is to combine multiple base partitions into a robust stable and accurate partition one of the key problems of clustering ensemble is how to exploit the cluster structure information in each base partition evidence accumulation is an effective framework which can convert the base partitions into a co association matrix this matrix describes the frequency of a pair of points partitioned into the same cluster but ignores some hidden information in the base partitions in this paper we reveal some of those information by refining the co association matrix from data point and base cluster level from the data point level as pairs of points in the same base cluster may have varied similarities their contributions to the co association matrix can be different from the cluster level since the base clusters may have diversified qualities the contribution of a base cluster as a whole can also be different from those of others after being refined the co association matrix is transformed into a path based similarity matrix so that more global information of the cluster structure is incorporated into the matrix finally spectral clustering is applied to the matrix to generate the final clustering result experimental results on 8 synthetic and 8 real data sets demonstrate that the clustering ensemble based on the refined co association matrix outperforms some state of the art clustering ensemble schemes highlightsa two level refined co association matrix for cluster ensemble is proposed the refined co association matrix is transformed by path based measure a theoretical background of the refinement is given the proposed method outperforms some state of the art ensemble methods

recovering the number of clusters in data sets with noise features using feature rescaling factors in this paper we introduce three methods for re scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical gaussian clusters with additional noise features our method obtains feature re scaling factors taking into account the structure of a given data set and the intuitive idea that different features may have different degrees of relevance at different clusters we experiment with the silhouette using squared euclidean manhattan and the pth power of the minkowski distance dunn s calinski harabasz and hartigan indexes on data sets with spherical gaussian clusters with and without noise features we conclude that our methods indeed increase the chances of estimating the true number of clusters in a data set

optimal marker placement for calculating the instantaneous center of rotation abstract a computer simulation with error propagation was performed to determine the optimal placement of marker points for calculating the instantaneous center of rotation cri the authors assume that planar rigid body motion occurs between two positions each defined by marker points noisy marker points were generated by perturbing their coordinates with random values from a normal population of errors the effects of these errors on the range of errors in calculating cri location were investigated parametric analysis determined that marker point placement had important effects on cri error marker placement was optimal when the estimated cri was located at the midpoint between the marker points while increasing the distance between marker points increased accuracy there is a critical distance above which no additional increase in accuracy was noted when using this placement the farther the marker midpoint was from the cri the greater was the error at these placements increasing the distance between the marker points continually decreased cri error the methodologies presented here help to improve the accuracy with which the location of the cri can be calculated however it is emphasized that the cri remains sensitive to noise and investigators should apply this kinematic parameter knowingly

cluster ensembles a knowledge reuse framework for combining multiple partitions this paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings we first identify several application scenarios for the resultant knowledge reuse framework that we call cluster ensembles the cluster ensemble problem is then formalized as a combinatorial optimization problem in terms of shared mutual information in addition to a direct maximization approach we propose three effective and efficient techniques for obtaining high quality combiners consensus functions the first combiner induces a similarity measure from the partitionings and then reclusters the objects the second combiner is based on hypergraph partitioning the third one collapses groups of clusters into meta clusters which then compete for each object to determine the combined clustering due to the low computational costs of our techniques it is quite feasible to use a supra consensus function that evaluates all three approaches against the objective function and picks the best solution for a given situation we evaluate the effectiveness of cluster ensembles in three qualitatively different application scenarios i where the original clusters were formed based on non identical sets of features ii where the original clustering algorithms worked on non identical sets of objects and iii where a common data set is used and the main purpose of combining multiple clusterings is to improve the quality and robustness of the solution promising results are obtained in all three situations for synthetic as well as real data sets

fuzzy partitions a way to integrate expert knowledge into distance calculations this work proposes a new pseudo metric based on fuzzy partitions fps this pseudo metric allows for the introduction of expert knowledge into distance computations performed on numerical data and can be used in various types of statistical clustering or other applications the knowledge is formalized by a fp in which each fuzzy set represents a linguistic concept the pseudo metric is designed to respect the fp semantics the univariate case is first studied and the pseudo metric behavior is discussed using synthetic experiments then a multivariate version is proposed as a minkowski like combination of univariate distances or semi distances the value of the proposal is illustrated with two real world case studies in the fields of biology and precision agriculture

comparing fuzzy probabilistic and possibilistic partitions when clustering produces more than one candidate to partition a finite set of objects o there are two approaches to validation i e selection of a best partition and implicitly a best value for c which is the number of clusters in o first we may use an internal index which evaluates each partition separately second we may compare pairs of candidates with each other or with a reference partition that purports to represent the true cluster structure in the objects this paper generalizes many of the classical indices that have been used with outputs of crisp clustering algorithms so that they are applicable for candidate partitions of any type i e crisp or soft with soft comprising the fuzzy probabilistic and possibilistic cases space prevents inclusion of all of the possible generalizations that can be realized this way here we concentrate on the rand index and its modifications we compare our fuzzy rand index with those of campello hullermeier and rifqi and brouwer and show that our extension of the rand index is o n while the other three are all o n2 numerical examples are given to illustrate various facets of the new indices in particular we show that our indices can be used even when the partitions are probabilistic or possibilistic and that our method of generalization is valid for any index that depends only on the entries of the classical i e four pair types contingency table for this problem

colored diffusion limited aggregation for urban migration abstract in this paper we study the growth probability and cluster morphologies which emerge in an off lattice two dimensional colored diffusion limited aggregation model for urban dynamics particularly migration to reach this goal three immobile interacting clusters that include the geographical concept of gravity are studied by exact enumeration in our simulations we find a strong correlation between the seed s distance migration rules and number of aggregated particles the growth probability of a certain angular subset and its rate and route of convergence to a normal distribution when migration cost is acting are also shown we search how all the factors mentioned above determine the cluster morphologies

calculation of the instantaneous centre of rotation for a rigid body abstract the mathematical procedure presented is used for the calculation of the instantaneous centre of rotation for a rigid body based on kinematic measurement data for every rotation angle interval observed an average centre of rotation is calculated by an appropriate optimizing calculation similar to data approximation by moving average practical applicability of this procedure has been proved by means of two planar and one three dimensional test measurements of known movements pure rotation rolling motion and motion of a ball and socket joint