Novel Biobjective Clustering (BiGC) Based on Cooperative Game Theory

We propose a new approach to clustering. Our idea is to map cluster formation to coalition formation in cooperative games, and to use the Shapley value of the patterns to identify clusters and cluster representatives. We show that the underlying game is convex and this leads to an efficient biobjective clustering algorithm that we call BiGC. The algorithm yields high-quality clustering with respect to average point-to-center distance (potential) as well as average intracluster point-to-point distance (scatter). We demonstrate the superiority of BiGC over state-of-the-art clustering algorithms (including the center based and the multiobjective techniques) through a detailed experimentation using standard cluster validity criteria on several benchmark data sets. We also show that BiGC satisfies key clustering properties such as order independence, scale invariance, and richness.

Cluster model of the glass transition

A cluster model of the glass transition has been developed, treating the relative size of the cluster as an order parameter. The model accounts for some of the features of the glass transition.

The validity of the Adler-Bardeen theorem in the supersymmetric U(1) gauge model

Using the dimensional reduction regularization scheme, we show that radiative corrections to the anomaly of the axial current, which is coupled to the gauge field, are absent in a supersymmetric U(1) gauge model for both 't Hooft-Veltman and Bardeen prescriptions for γ5. We also discuss the results with reference to conventional dimensional regularization. This result has significant implications with respect to the renormalizability of supersymmetric models.

Cluster analysis of rainfall stations of the Indian peninsula

Principal component analysis is applied to derive patterns of temporal variation of the rainfall at fifty-three stations in peninsular India. The location of the stations in the coordinate space determined by the amplitudes of the two leading eigenvectors is used to delineate them into eight clusters. The clusters obtained seem to be stable with respect to variations in the grid of stations used. Stations within any cluster occur in geographically contiguous areas.

High pressure transport behaviour of AgI-Ag2O-MoO3 glasses and the cluster model

The effect of pressure on the conductivity of fast ion conducting AgI-Ag2O-MoO3 glasses has been investigated down to 150 K. The observed variation of conductivities appears to support the application of cluster model to the ionic glasses.

Glass transition. A new approach based on cluster model of glasses

The structure of real glasses has been considered to be microheterogeneous, composed of clusters and connective tissue. Particles in the cluster are assumed to be highly correlated in positions. The tissue is considered to have a truly amorphous structure with its particles vibrating in highly anharmonic potentials. Glass transition is recognized as corresponding to the melting of clusters. A simple mathematical model has been developed which accounts for various known features associated with glass transition, such as range of glass transition temperature,T g, variation ofT g with pressure, etc. Expressions for configurational thermodynamic properties and transport properties of glass forming systems are derived from the model. The relevence and limitations of the model are also discussed.

Synthesis and characterization of a rare hemiacetalate bridged incomplete View the MathML source dicubane cluster

Reaction of 2-pyridinecarboxaldehyde [(Py)CHO] with Cu(NO3)2·2.5H2O in the presence of 4-aminopyridine and NaN3 in MeOH lead to an incomplete double-cubane [Cu4{PyCH(O)(OMe)}4(N3)4] (1) in 87% isolated yield, representing a rare type of metal cluster containing bridging hemiacetalate ligand [pyCH(O)(OMe)]−1 which was characterized by single crystal structure analysis and variable temperature magnetic behavior.

Electrochemisorption - A Cluster Approach

A simple semiempirical quantum chemical approach (Extended Huckel Theory) is shown to give a reasonable description of the electronic structural aspects of chemisorption on the mercury model surface. Chemisorptive interaction of alkali metal atoms and cations, halogen atoms and anions, and water molecules with a charge-neutralized hexagonal close-packed cluster of seven Hg atoms is studied. Adsorption of H, C, N and O atoms on the same model cluster is studied for comparison with earlier work. Chemisorption energies, charge transfer, interaction distance and hydration effects are discussed and compared with experimental results where available.

An HI study of three long-tailed irregular galaxies in the cluster Abell 1367

We present the results on the distribution and kinematics of HI gas with higher sensitivity and in one case of higher spectral resolution as well than reported earlier, of three irregular galaxies CGCG 097073, 097079 and 097087 (UGC 06697) in the cluster Abell 1367. These galaxies are known to exhibit long (50 - 75 kpc) tails of radio continuum and optical emission lines (H alpha) pointing away from the cluster centre and arcs of starformation on the opposite sides of the tails, These features as well as the HI properties, with two of the galaxies (CGCG 097073 and 097079) exhibiting sharper gradients in HI intensity on the side of the tails, are consistent with the HI gas being affected by the ram pressure of the intracluster medium. However the HI emission in all the three galaxies extends to much smaller distances than the radio-continuum and H alpha tails, and are possibly still bound to the parent galaxies. Approximately 20 - 30 per cent of the HI mass is seen to accumulate on the downstream side due to the effects of ram pressure.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Stability of multi-cluster synchronization

A system of many coupled oscillators on a network can show multicluster synchronization. We obtain existence conditions and stability bounds for such a multicluster synchronization. When the oscillators are identical, we obtain the interesting result that network structure alone can cause multicluster synchronization to emerge even when all the other parameters are the same. We also study occurrence of multicluster synchronization when two different types of oscillators are coupled.

Electronic structure and bonding of beta-rhombohedral boron using cluster fragment approach

Theoretical studies using density functional theory are carried out to understand the electronic structure and bonding and electronic properties of elemental beta-rhombohedral boron. The calculated band structure of ideal beta-rhombohedral boron (B-105) shows valence electron deficiency and depicts metallic behavior. This is in contrast to the experimental result that it is a semiconductor. To understand this ambiguity we discuss the electronic structure and bonding of this allotrope with cluster fragment approach using our recently proposed mno rule. This helps us to comprehend in greater detail the structure of B-105 and materials which are closely related to beta-rhombohedral boron. The molecular structures B12H12-2, B28H21+1, BeB27H21, LiB27H21-1, CB27H21+2, B57H36+3, Be3B54H36, and Li2CB54H36, and corresponding solids Li8Be3B102 and Li10CB102 are arrived at using these ideas and studied using first principles density functional theory calculations.

Cluster based core vector machine

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.

Dynamical cluster approximation: Nonlocal dynamics of correlated electron systems

We recently introduced the dynamical cluster approximation (DCA), a technique that includes short-ranged dynamical correlations in addition to the local dynamics of the dynamical mean-field approximation while preserving causality. The technique is based on an iterative self-consistency scheme on a finite-size periodic cluster. The dynamical mean-field approximation (exact result) is obtained by taking the cluster to a single site (the thermodynamic limit). Here, we provide details of our method, explicitly show that it is causal, systematic, Phi derivable, and that it becomes conserving as the cluster size increases. We demonstrate the DCA by applying it to a quantum Monte Carlo and exact enumeration study of the two-dimensional Falicov-Kimball model. The resulting spectral functions preserve causality, and the spectra and the charge-density-wave transition temperature converge quickly and systematically to the thermodynamic limit as the cluster size increases.