Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Support vector clustering-based direct coherency identification of generators in a multi-machine power system

This study investigates the application of support vector clustering (SVC) for the direct identification of coherent synchronous generators in large interconnected multi-machine power systems. The clustering is based on coherency measure, which indicates the degree of coherency between any pair of generators. The proposed SVC algorithm processes the coherency measure matrix that is formulated using the generator rotor measurements to cluster the coherent generators. The proposed approach is demonstrated on IEEE 10 generator 39-bus system and an equivalent 35 generators, 246-bus system of practical Indian southern grid. The effect of number of data samples and fault locations are also examined for determining the accuracy of the proposed approach. An extended comparison with other clustering techniques is also included, to show the effectiveness of the proposed approach in grouping the data into coherent groups of generators. This effectiveness of the coherent clusters obtained with the proposed approach is compared in terms of a set of clustering validity indicators and in terms of statistical assessment that is based on the coherency degree of a generator pair.

H.264 COMPRESSED VIDEO CLASSIFICATION USING HISTOGRAM OF ORIENTED MOTION VECTORS (HOMV)

In this paper, we have proposed a simple and effective approach to classify H.264 compressed videos, by capturing orientation information from the motion vectors. Our major contribution involves computing Histogram of Oriented Motion Vectors (HOMV) for overlapping hierarchical Space-Time cubes. The Space-Time cubes selected are partially overlapped. HOMV is found to be very effective to define the motion characteristics of these cubes. We then use Bag of Features (B OF) approach to define the video as histogram of HOMV keywords, obtained using k-means clustering. The video feature, thus computed, is found to be very effective in classifying videos. We demonstrate our results with experiments on two large publicly available video database.

Novel spherical hierarchical structures of GdOOH and Eu:GdOOH: rapid microwave-assisted synthesis through self-assembly, thermal conversion to oxides, and optical studies

We report a novel, rapid, and low-temperature method for the synthesis of undoped and Eu-doped GdOOH spherical hierarchical structures, without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse microspheres measuring about 1.3 mu m in diameter. Electron microscopy reveals that each microsphere is an assembly of two-dimensional nanoflakes (about 30 nm thin) which, in turn, result from the assembly of crystallites measuring about 9 nm in diameter. Thus, a three-level hierarchy can be seen in the formation of the GdOOH microspheres: from nanoparticles to 2D nanoflakes to 3D spherical structures. When doped with Eu3+ ions, the GdOOH microspheres show a strong red emission, making them promising candidates as phosphors. Finally, thermal conversion at modest temperatures leads to the formation of corresponding oxide structures with enhanced luminescence, while retaining the spherical morphology of their oxyhydroxide precursor.

Green ionothermal synthesis of hierarchical nanostructures of SnS2 and their Li-ion storage properties

Flower-like hierarchical architectures of layered SnS2 have been synthesized ionothermally for the first time, using a water soluble EMIM]BF4 ionic liquid (IL) as the solvent medium. At lower reaction temperatures, the hierarchical structures are formed of few-layered polycrystalline 2D nanosheet-petals composed of randomly oriented nanoparticles of SnS2. The supramolecular networks of the IL serve as templates on which the nanoparticles of SnS2 are glued together by combined effects of hydrogen bonding, electrostatic, hydrophobic and imidazolium stacking interactions of the IL, giving rise to polycrystalline 2D nanosheet-petals. At higher reaction temperatures, single crystalline plate-like nanosheets with well-defined crystallographic facets are obtained due to rapid inter-particle diffusion across the IL. Efficient surface charge screening by the IL favors the aggregation of individual nanosheets to form hierarchical flower-like architectures of SnS2. The mechanistic aspects of the ionothermal bottom-up hierarchical assembly of SnS2 nanosheets are discussed in detail. Li-ion storage properties of the pristine SnS2 samples are examined and the electrochemical performance of the sample synthesized at higher temperatures is found to be comparable to that reported for pristine SnS2 samples in the literature.

Regional flood frequency analysis using kernel- based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Microspherical, hierarchical structures of blue-green-emitting Dy:GdOOH and Dy:Gd2O3

Dy-doped GdOOH microspherical structures were prepared in minutes without using any structure-directing agents, through the microwave irradiation route. The as-prepared product consists of nearly monodisperse sphere-like entities with each one representing a three-level hierarchy in its formation. Dy:GdOOH powder samples show a bright blue-green luminescence under UV excitation, making these structures potentially important in the field of optical and luminescent devices. Finally, thermal conversion to the corresponding oxide structures occurs at modest temperatures, spherical morphology intact and with enhanced luminescence behaviour. (C) 2014 Elsevier B.V. All rights reserved.

Multiscale Symmetry Detection in Scalar Fields by Clustering Contours

The complexity in visualizing volumetric data often limits the scope of direct exploration of scalar fields. Isocontour extraction is a popular method for exploring scalar fields because of its simplicity in presenting features in the data. In this paper, we present a novel representation of contours with the aim of studying the similarity relationship between the contours. The representation maps contours to points in a high-dimensional transformation-invariant descriptor space. We leverage the power of this representation to design a clustering based algorithm for detecting symmetric regions in a scalar field. Symmetry detection is a challenging problem because it demands both segmentation of the data and identification of transformation invariant segments. While the former task can be addressed using topological analysis of scalar fields, the latter requires geometry based solutions. Our approach combines the two by utilizing the contour tree for segmenting the data and the descriptor space for determining transformation invariance. We discuss two applications, query driven exploration and asymmetry visualization, that demonstrate the effectiveness of the approach.

Formation of hierarchical structures of Fe2O3 by the liquid-liquid interface technique

We report the formation of dendritic hierarchical structures of alpha-Fe2O3 and nanostructures of Fe2O3 by the simple liquid-liquid interface method. The morphology of thin films determined by high-resolution scanning electron microscopy shows nanorods, nanosheets and dendritic Fe2O3. The identification of phases of iron oxide structures is carried out by using XRD and XPS studies. XRD and XPS measurements point out the highly crystalline dendritic alpha-Fe2O3 phase and the mixed phase of alpha- and gamma-Fe2O3 nanostructures. The magnetic measurement also suggests the presence of a mixed phase in the sample grown for 72 hours.

Analysis of the hierarchical structure of the B. subtilis transcriptional regulatory network

The transcriptional regulation of gene expression is orchestrated by complex networks of interacting genes. Increasing evidence indicates that these `transcriptional regulatory networks' (TRNs) in bacteria have an inherently hierarchical architecture, although the design principles and the specific advantages offered by this type of organization have not yet been fully elucidated. In this study, we focussed on the hierarchical structure of the TRN of the gram-positive bacterium Bacillus subtilis and performed a comparative analysis with the TRN of the gram-negative bacterium Escherichia coli. Using a graph-theoretic approach, we organized the transcription factors (TFs) and sigma-factors in the TRNs of B. subtilis and E. coli into three hierarchical levels (Top, Middle and Bottom) and studied several structural and functional properties across them. In addition to many similarities, we found also specific differences, explaining the majority of them with variations in the distribution of s-factors across the hierarchical levels in the two organisms. We then investigated the control of target metabolic genes by transcriptional regulators to characterize the differential regulation of three distinct metabolic subsystems (catabolism, anabolism and central energy metabolism). These results suggest that the hierarchical architecture that we observed in B. subtilis represents an effective organization of its TRN to achieve flexibility in response to a wide range of diverse stimuli.

A transmission electron microscopy and X-ray photoelectron spectroscopy study of annealing induced gamma-phase nucleation, clustering, and interfacial dynamics in reactively sputtered amorphous alumina thin films

Pure alpha-Al2O3 exhibits a very high degree of thermodynamical stability among all metal oxides and forms an inert oxide scale in a range of structural alloys at high temperatures. We report that amorphous Al2O3 thin films sputter deposited over crystalline Si instead show a surprisingly active interface. On annealing, crystallization begins with nuclei of a phase closely resembling gamma-Alumina forming almost randomly in an amorphous matrix, and with increasing frequency near the substrate/film interface. This nucleation is marked by the signature appearance of sharp (400) and (440) reflections and the formation of a diffuse diffraction halo with an outer maximal radius of approximate to 0.23 nm enveloping the direct beam. The microstructure then evolves by a cluster-coalescence growth mechanism suggestive of swift nucleation and sluggish diffusional kinetics, while locally the Al ions redistribute slowly from chemisorbed and tetrahedral sites to higher anion coordinated sites. Chemical state plots constructed from XPS data and simple calculations of the diffraction patterns from hypothetically distorted lattices suggest that the true origins of the diffuse diffraction halo are probably related to a complex change in the electronic structure spurred by the a-gamma transformation rather than pure structural disorder. Concurrent to crystallization within the film, a substantially thick interfacial reaction zone also builds up at the film/substrate interface with the excess Al acting as a cationic source. (C) 2015 AIP Publishing LLC.

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.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.

Spectral Clustering with Jensen-type kernels and their multi-point extensions

Motivated by multi-distribution divergences, which originate in information theory, we propose a notion of `multipoint' kernels, and study their applications. We study a class of kernels based on Jensen type divergences and show that these can be extended to measure similarity among multiple points. We study tensor flattening methods and develop a multi-point (kernel) spectral clustering (MSC) method. We further emphasize on a special case of the proposed kernels, which is a multi-point extension of the linear (dot-product) kernel and show the existence of cubic time tensor flattening algorithm in this case. Finally, we illustrate the usefulness of our contributions using standard data sets and image segmentation tasks.