Artificial Clustering Research Articles

DEATH OF A CLUSTER: THE DESTRUCTION OF M67 AS SEEN BY THE SLOAN DIGITAL SKY SURVEY

We probe the spatial and dynamical structure of the old open cluster M67 using photometric data from the Sloan Digital Sky Survey's sixth data release. Making use of an optimal contrast, or matched filter, algorithm, we map the distribution of high probability members of M67. We find an extended and elongated halo of likely members to a radius of nearly 60'. Our measured core radius of Rcore = 8.'24+/-0.'60 is somewhat larger than that of previous estimates. We attribute the larger core radius measurement to the SDSS probing lower mass main sequence stars than has been done before for similar studies of M67, and the exclusion of post main sequence M67 members in the SDSS sample. We estimate the number of M67 members in our SDSS sample to be 1385+/-67 stars. A lower limit on the binary fraction in M67 is measured to be 45%. A higher fraction of binary stars is measured in the core as compared to the halo, and the luminosity function of the core is found to be more depleted of low-mass stars. Thus the halo is consistent with mass segregation within the cluster. The galactic orbit of M67 is calculated from recent proper motion and radial velocity determinations. The elongated halo is roughly aligned to the proper motion of the cluster. This appears to be a result of mass segregation due to the galactic tidal field. Our algorithm is run on 2MASS photometry to directly compare to previous studies of M67. Decreasing core radii are found for stars with greater masses. We test the accuracy of our algorithm using 1000 artificial cluster Monte Carlo simulations. It is found that the matched filter technique is suitable for recovering low-density spatial structures, as well as measuring the binary fraction of the cluster.

Study of Modularity-based Fuzzy Immune Network Clustering Algorithm and Its Application in Fault Diagnosis

A new immune network clustering method with the antibody mutation strategy and the idiotypic network regulation strategy is proposed to improve the data compression ratio and the classification accuracy of the immune network clustering algorithm. Firstly, a novel idiotypic fuzzy immune recognition hypersphere is constructed to update the antibody network based on the Timmis’s Artificial Recognition Ball and the fuzzy theory. In addition, according to the modularity concept which can describe the community structure of the complex network effectively, a modularity-based clustering criterion function is designed, and a new algorithm based on the modularity merging for extracting the memory antibody is proposed to realize the self-adaptive compression for the antibody network. Secondly, a new fuzzy immune network clustering algorithm is developed, which combines the artificial immune network clustering strategy with the modularity-based criterion function. The validity of the method is verified with the UCI data sets. Results show that the proposed method can obtain the reasonable memory antibody network and improve the data compression ratio and the classification accuracy. Finally, the method is applied to the fault diagnosis of a four-stage reciprocal compressor. Compared with the aiNet and fuzzy artificial immune network classification method (FAINC), the proposed method is superior in the classification accuracy and the compression ratio.

An artificial bee colony approach for clustering

Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb’s rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K–NM–PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required.

An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units

This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN GA algorithm is able to find a stochastic frontier based on a set of input---output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). Also, in this algorithm, GA is used to cluster DMUs to increase DMUs' homogeneousness. It should be noted that data envelopment analysis (DEA) is sensitive to the presence of the outliers and statistical noise. It is also not capable of performing prediction and forecasting. This is shown by two examples related to outlier situations. However, the proposed algorithm is capable of handling outliers and noise and DEA is used as a benchmark to show advantages of the proposed algorithm. Also, the proposed algorithm and conventional algorithm are compared in viewpoint of DEA through statistical t-test. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.

Storm surge prediction using an artificial neural network model and cluster analysis

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.

Combinatorial biosynthesis of plant medicinal polyketides by microorganisms

Combinatorial biosynthesis includes an approach in which genes from different organisms are assembled to design and construct an artificial gene cluster for production of bioactive compounds. An Escherichia coli system carrying artificial biosynthetic pathways for production of plant-specific medicinal polyketides, such as flavonoids, stilbenoids, isoflavonoids, and curcuminoids, was designed and expressed. Starting with amino acids tyrosine and phenylalanine as substrates, this system yielded, for example, naringenin, resveratrol, genistein, and curcumin. Supplementation of unnatural carboxylic acids as precursors to the E. coli cells led to production of unnatural compounds. Addition of modification enzymes to the artificial pathways led to production of natural and unnatural flavonols, flavones, and methylated resveratrols. This microbial system is promising not only for construction of larger libraries by employing other polyketide synthases and modification enzymes of various origins as members of the artificial pathway but also for efficient use of the potential of the host microorganisms.

Artificial immune kernel clustering network for unsupervised image segmentation

An immune kernel clustering network (IKCN) is proposed based on the combination of the artificial immune network and the support vector domain description (SVDD) for the unsupervised image segmentation. In the network, a new antibody neighborhood and an adaptive learning coefficient, which is inspired by the long-term memory in cerebral cortices are presented. Starting from IKCN algorithm, we divide the image feature sets into subsets by the antibodies, and then map each subset into a high dimensional feature space by a mercer kernel, where each antibody neighborhood is represented as a support vector hypersphere. The clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree (MST), where a predefined number of clustering is not needed. We compare the proposed methods with two common clustering algorithms for the artificial synthetic data set and several image data sets, including the synthetic texture images and the SAR images, and encouraging experimental results are obtained.

Machine learning and data mining: introduction to principles and algorithms

Introduction Learning and intelligence Machine learning basics Knowledge representation Learning as search Attribute quality measures Data pre-processing Constructive induction Symbolic learning Statistical learning Artificial neural networks Cluster analysis Learning theory Computational learning theory Definitions References and index.

Fluorescence correlation spectroscopy and photon counting histogram on membrane proteins: functional dynamics of the glycosylphosphatidylinositol-anchored urokinase plasminogen activator receptor.

The oligomerization of glycosylphosphatidylinositol-anchored proteins is thought to regulate their association with membrane microdomains, subcellular sorting, and activity. However, these mechanisms need to be comprehensively explored in living, unperturbed cells, without artificial clustering agents, and using fluorescent protein-tagged chimeras that are fully biologically active. We expressed in human embryo kidnay 293 (HEK293) cells a biologically active chimera of the urokinase plasminogen activator receptor (uPAR), the uPAR-mEGFP-GPI. We also produced HEK293/D2D3-mEGFP-GPI cells expressing the truncated form of the receptor, lacking biological activity. We studied the dynamics and oligomerization of the two proteins, combining fluorescence correlation spectroscopy (FCS) and photon counting histogram (PCH) analyses, and using subclones with homogenously low expression levels. Overall, the mobile fractions of the two proteins, constituted by monomers and dimers, had comparable diffusion coefficients. However, the diffusion coefficient decreased in monomer-enriched fractions only for the active receptor, suggesting that uPAR monomers might be preferentially engaged in multiprotein transmembrane signaling complexes. Our approach helps in limiting the alteration of the data due to out-of-focus effects and in minimizing the overestimation of the molecular brightness. In addition to a careful design of the cellular model, it gives reliable estimates of diffusion coefficients and oligomerization of GPI-anchored proteins, in steady-state conditions, at low expression levels, and in live, unperturbed cells.

Artificial Neural Network Study of Whole-Cell Bacterial Bioreporter Response Determined Using Fluorescence Flow Cytometry

Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

Artificial neural networks and cluster analysis in landslide susceptibility zonation

A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.

Mining and Polishing of the Treasure Trove in the Bacterial GenusStreptomyces

The complex morphogenesis of the bacterial genus Streptomyces has made this genus a model prokaryote for study of multicellular differentiation, and its ability to produce a wide variety of secondary metabolites has made it an excellent supplier of biologically active substances, including antibiotics. This review summarizes our study of these two characteristics of Streptomyces, focusing on the A-factor regulatory cascade and work derived from the A-factor study. A microbial hormone, A-factor (2-isocapryloyl-3R-hydroxymethyl-gamma-butyrolactone), triggers morphological differentiation and secondary metabolism in Streptomyces griseus. The key steps in the A-factor regulatory cascade, including afsA, encoding the key enzyme for A-factor biosynthesis, arpA, encoding the A-factor receptor, and adpA, encoding a transcriptional activator, are elucidated. The target genes of the regulatory cascade include genes of various functions required for morphological development and secondary metabolite formation. The biosynthesis gene clusters for grixazone and hexahydroxyperylenequinone are examples. The former contains the enzymes for novel benzene ring formation and phenoxazinone formation, and the latter contains enzymes belonging to a type III polyketide synthase and a cytochrome P-450. Enzymes of various catalytic functions in Streptomyces are useful as members of an artificial gene cluster constructed in Escherichia coli for fermentative production of plant-specific flavonoids, including isoflavones and unnatural compounds.

Artificial immune system inspired behavior-based anti-spam filter

This paper proposes a novel behavior-based anti-spam technology for email service based on an artificial immune-inspired clustering algorithm. The suggested method is capable of continuously delivering the most relevant spam emails from the collection of all spam emails that are reported by the members of the network. Mail servers could implement the anti-spam technology by using the “black lists” that have been already recognized. Two main concepts are introduced, which defines the behavior-based characteristics of spam and to continuously identify the similar groups of spam when processing the spam streams. Experiment results using real-world datasets reveal that the proposed technology is reliable, efficient and scalable. Since no single technology can achieve one hundred percent spam detection with zero false positives, the proposed method may be used in conjunction with other filtering systems to minimize errors.

Artificial Neural Networks and Clustering Techniques Applied in the Reconfiguration of Distribution Systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

Model checking for additive hazards model with multivariate survival data

Multivariate failure time data often arise in biomedical studies due to natural or artificial clustering. With appropriate adjustment for the underlying correlation, the marginal additive hazards model characterizes the hazard difference via a linear link function between the hazard and covariates. We propose a class of graphical and numerical methods to assess the overall fitting adequacy of the marginal additive hazards model. The test statistics are based on the supremum of the stochastic processes derived from the cumulative sum of the martingale-based residuals over time and/or covariates. The distribution of the stochastic process can be approximated through a simulation technique. The proposed tests examine how unusual the observed stochastic process is, compared to a large number of realizations from the approximated process. This class of tests is very general and suitable for various purposes of model fitting evaluation. Simulation studies are conducted to examine the finite sample performance, and the model-checking methods are illustrated with data from an otitis media study.

Artificial neural networks classification and clustering of methodologies and applications – literature analysis from 1995 to 2005

Abstract Based on a scope of 10,120 articles on ANNs, this paper uses data mining including association rules and cluster analysis, to survey these ANNs papers through keyword classification and clustering of articles from 1995 to 2005, exploring the ANNs methodologies and application developments during that period. The four decision variables of keywords, author’s nationality, research category, and year of publication, are implemented for data mining with total of 110,080 data items. The research findings show that some specific ANNs methodologies and applications pattern can be extracted from the mining results, and these describe the ANNs development over this period. In addition, using more data mining approaches for analysis could provide different explanations for ANNs development. Finally, discussion and brief conclusion are presented.

Enhanced Resonant Raman Scattering and Electron−Phonon Coupling from Self-Assembled Secondary ZnO Nanoparticles

Self-assembled secondary ZnO nanoparticles, recognized with the agglomeration of crystalline subcrystals, are successfully synthesized by a simple sol-gel method. TEM images display that one artificial cluster behaves in a single-crystal-like wurtzite structure because subcrystals coagulate as the same crystal orientation. Moreover, from the resonant Raman scattering, the as-grown sample exhibits phonon red shift; meanwhile, the coupling strength between electron and longitudinal optical phonon, determined by the ratio of second- to first-order Raman scattering cross sections, diminishes compared with the samples after postannealing at 350 and 500 degrees C. The size dependence of electron-phonon coupling is principally as a result of the Fröhlich interaction.

Learning multicriteria fuzzy classification method PROAFTN from data

In this paper, we present a new methodology for learning parameters of multiple criteria classification method PROAFTN from data. There are numerous representations and techniques available for data mining, for example decision trees, rule bases, artificial neural networks, density estimation, regression and clustering. The PROAFTN method constitutes another approach for data mining. It belongs to the class of supervised learning algorithms and assigns membership degree of the alternatives to the classes. The PROAFTN method requires the elicitation of its parameters for the purpose of classification. Therefore, we need an automatic method that helps us to establish these parameters from the given data with minimum classification errors. Here, we propose variable neighborhood search metaheuristic for getting these parameters. The performances of the newly proposed method were evaluated using 10 cross validation technique. The results are compared with those obtained by other classification methods previously reported on the same data. It appears that the solutions of substantially better quality are obtained with proposed method than with these former ones.

Bayesian Cure Rate Frailty Models with Application to a Root Canal Therapy Study

Due to natural or artificial clustering, multivariate survival data often arise in biomedical studies, for example, a dental study involving multiple teeth from each subject. A certain proportion of subjects in the population who are not expected to experience the event of interest are considered to be "cured" or insusceptible. To model correlated or clustered failure time data incorporating a surviving fraction, we propose two forms of cure rate frailty models. One model naturally introduces frailty based on biological considerations while the other is motivated from the Cox proportional hazards frailty model. We formulate the likelihood functions based on piecewise constant hazards and derive the full conditional distributions for Gibbs sampling in the Bayesian paradigm. As opposed to the Cox frailty model, the proposed methods demonstrate great potential in modeling multivariate survival data with a cure fraction. We illustrate the cure rate frailty models with a root canal therapy data set.

Efficient production of (2S)-flavanones by Escherichia coli containing an artificial biosynthetic gene cluster

For the fermentative production of plant-specific flavanones (naringenin, pinocembrin) by Escherichia coli, a plasmid was constructed which carried an artificial biosynthetic gene cluster, including PAL encoding a phenylalanine ammonia-lyase from a yeast, ScCCL encoding a cinnamate/coumarate:CoA ligase from the actinomycete Streptomyces coelicolor A3(2), CHS encoding a chalcone synthase from a licorice plant and CHI encoding a chalcone isomerase from the Pueraria plant. The recombinant E. coli cells produced (2S)-naringenin from tyrosine and (2S)-pinocembrin from phenylalanine. When the two subunit genes of acetyl-CoA carboxylase from Corynebacterium glutamicum were expressed under the control of the T7 promoter and the ribosome-binding sequence in the recombinant E. coli cells, the flavanone yields were greatly increased, probably because enhanced expression of acetyl-CoA carboxylase increased a pool of malonyl-CoA that was available for flavanone synthesis. Under cultural conditions where E. coli at a cell density of 50 g/l was incubated in the presence of 3 mM tyrosine or phenylalanine, the yields of naringenin and pinocembrin reached about 60 mg/l. The fermentative production of flavanones in E. coli is the first step in the construction of a library of flavonoid compounds and un-natural flavonoids in bacteria.