The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.

The New Software Package for Dynamic Hierarchical Clustering for Circles Types of Shapes

In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.

Using the Agglomerative Method of Hierarchical Clustering as a Data Mining Tool in Capital Market

The purpose of this paper is to explain the notion of clustering and a concrete clustering method- agglomerative hierarchical clustering algorithm. It shows how a data mining method like clustering can be applied to the analysis of stocks, traded on the Bulgarian Stock Exchange in order to identify similar temporal behavior of the traded stocks. This problem is solved with the aid of a data mining tool that is called XLMiner™ for Microsoft Excel Office.

A Partition Metric for Clustering Features Analysis

A new distance function to compare arbitrary partitions is proposed. Clustering of image collections and image segmentation give objects to be matched. Offered metric intends for combination of visual features and metadata analysis to solve a semantic gap between low-level visual features and high-level human concept.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

A Statistical Approach for Multilingual Document Clustering and Topic Extraction from Clusters

2000 Mathematics Subject Classification: 62H30

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.

Dark matter in dwarf irregular galaxies, DDO 210 and DDO 169

It has been proposed that dwarf irregular galaxies can be separated into two classes based on their formation mechanism; that they are the result of the collapse of a primordial gas cloud or that they are the product of condensation of gas in the tidal tails of interacting galaxies. Simulations of galaxy interactions indicate that one can differentiate between these two scenarios by the dark matter content, with a low dark matter content indicating a fossil tidal dwarf. The purpose of this dissertation was to explore the dark matter distribution of two dwarf irregular galaxies using optical and neutral atomic hydrogen data. For DDO 210, the method of mass-modelling was used to determine its dark matter. About 64% of the galaxy mass was calculated to be in the form of dark matter and hence it is unlikely to be a fossil tidal dwarf. The method of mass-modelling could not be used for DDO 169 as the galaxy shows evidence of being tidally disrupted and hence, has a disturbed velocity field. Instead, the suggestion that dark matter might be responsible for a pressure anomaly in DDO 169 was tested to determine its dark matter content. According to this method, a pressure anomaly does exist but without a concrete value for the scale-height, it is unclear whether the anomaly is due to the presence of dark matter. Hence one cannot say how much dark matter might actually be present in DDO 169. A rotation curve would be required to do this. ^

The Study of Clustering of Taiwanese Tourists' Motivations to Hong Kong

Abstract Driven by the political and economic forces of cross-strait, Taiwan has become one of the major source markets for Hong Kong tourism industry since 1987. The major purposes of this study were to investigate the following factors (1) The influential factors of travel motivation, (2) The clusters of travel motivations, (3) The marketing segmentation of clusters of Taiwanese tourists to visit Hong Kong. Through ten travel agents, self-report surveys were distributed to collect data from 366 Taiwanese travelers. Hence, four push factors and six pull factors were identified as travel motivations through the factor analysis. Combined with the cluster analysis; five new groups were founded. Finally, five clusters which process unique profiles (location difference, visiting frequency, travel satisfaction, and destination loyalty) were addressed. The suggestions of developing effective market strategies to attract Taiwanese tourists to Hong Kong were also provided.

The H I chronicles of LITTLE THINGS blue compact dwarf galaxies

Star formation occurs when the gas (mostly atomic hydrogen; H I) in a galaxy becomes disturbed, forming regions of high density gas, which then collapses to form stars. In dwarf galaxies it is still uncertain which processes contribute to star formation and how much they contribute to star formation. Blue compact dwarf (BCD) galaxies are low mass, low shear, gas rich galaxies that have high star formation rates when compared to other dwarf galaxies. What triggers the dense burst of star formation in BCDs but not other dwarfs is not well understood. It is often suggested that BCDs may have their starburst triggered by gravitational interactions with other galaxies, dwarf-dwarf galaxy mergers, or consumption of intergalactic gas. However, there are BCDs that appear isolated with respect to other galaxies, making an external disturbance unlikely.^ Here, I study six apparently isolated BCDs from the LITTLE THINGS sample in an attempt to understand what has triggered their burst of star formation. LITTLE THINGS is an H I survey of 41 dwarf galaxies. Each galaxy has high angular and velocity resolution H I data from the Very Large Array (VLA) telescope and ancillary stellar data. I use these data to study the detailed morphology and kinematics of each galaxy, looking for signatures of starburst triggers. In addition to the VLA data, I have collected Green Bank Telescope data for the six BCDs. These high sensitivity, low resolution data are used to search the surrounding area of each galaxy for extended emission and possible nearby companion galaxies.^ The VLA data show evidence that each BCD has likely experienced some form of external disturbance despite their apparent isolation. These external disturbances potentially seen in the sample include: ongoing/advanced dwarf-dwarf mergers, an interaction with an unknown external object, and external gas consumption. The GBT data result in no nearby, separate H I companions at the sensitivity of the data. These data therefore suggest that even though these BCDs appear isolated, they have not been evolving in isolation. It is possible that these external disturbances may have triggered the starbursts that defines them as BCDs.^

Angular momentum of S0 galaxies

The aim of this thesis is to study the angular momentum of a sample of S0 galaxies. In the quest to understand whether the formation of S0 galaxies is more closely linked to that of ellipticals or that of spirals, our goal is to compare the amount of their specific angular momentum as a function of stellar mass with respect to spirals. Through kinematic comparison between these different classes of galaxies we aim to understand if a scenario of passive evolution, in which the galaxy’s gas is consumed and the star formation is quenched, can be considered as plausible mechanism to explain the transformation from spirals to S0s. In order to derive the structural and photometric parameters of galaxy sub-components we performed a bulge-disc decomposition of optical images using GALFIT. The stellar kinematic of the galaxies was measured using integral field spectroscopic data from CALIFA survey. The development of new original software, based on a Monte Carlo Markov Chain algorithm, allowed us to obtain the values of the line of sight velocity and velocity dispersion of disc and bulge components. The result that we obtained is that S0 discs have a distribution of stellar specific angular momentum that is in full agreement with that of spiral discs, so the mechanism of simple fading can be considered as one of the most important for transformation from spirals to S0s.

High resolution study of narrow tailed radio galaxies in clusters

Clusters of galaxies are the most massive and large gravitationally bounded systems in the whole Universe. Their study is of fundamental importance to constrain cosmological parameters and to obtain informations regarding various kind of emission in different wavebands. In particular, in the radio domain, beside the diffuse emission, the study is focused on the radio galaxies emission. Radio galaxies in clusters can have peculiar morphology, since they interact with the intracluster medium (ICM) in which they are embedded. Particularly, in this thesis we focused our attention on the so-called Narrow-Angle Tailed radio galaxies (NAT), which present radio jets that are bent at extreme angle, up to 90 degrees, from their original orientation. Some NAT show a narrow extended structure and the two radio tails are not resolved even with high resolution radio observations. An example is provided by the source IC310, in the Perseus Cluster, whose structure has been recently interpreted as due to Doppler boosting effects of a relativistic jet oriented at a small angle with respect to the line of sight. If the structure is due to relativistic effects, this implies that the jets are relativistic at about 400 kpc from the core, but this is in contrast with unified models, which predict that for low-power radio source (NAT are classified as FRI radio galaxies) the jets decelerate to sub-relativistic speed within a few kpc from the core. To investigate this scientific topic, in this thesis we have analyzed the innermost structure of a sample of eleven radio galaxies showing a very narrow NAT structure. We can conclude that the structure of these radio galaxies is different from that of IC310. These radio galaxies are indeed strongly influenced by environmental effects and are similar to classical NAT sources.

Heavily obscured AGN: an ideal laboratory to study the early co-evolution of galaxies and black holes

Obscured AGN are a crucial ingredient to understand the full growth history of super massive black holes and the coevolution with their host galaxies, since they constitute the bulk of the BH accretion. In the distant Universe, many of them are hosted by submillimeter galaxies (SMGs), characterized by a high production of stars and a very fast consumption of gas. Therefore, the analysis of this class of objects is fundamental to investigate the role of the ISM in the early coevolution of galaxies and black holes. We present a multiwavelength study of a sample of six obscured X-ray selected AGN at z>2.5 in the CDF-S, detected in the far-IR/submm bands. We performed the X-ray spectral analysis based on the 7Ms Chandra dataset, which provides the best X-ray spectral information currently available for distant AGN. We were able to place constraints on the obscuring column densities and the intrinsic luminosities of our targets. Moreover, we built up the UV to FIR spectral energy distributions (SEDs) by combining the broad-band photometry from CANDELS and the Herschel catalogs, and analyzed them by means of an SED decomposition technique. Therefore, we derived important physical parameters of both the host galaxy and the AGN. In addition, we obtained, through an empirical calibration, the gas mass in the host galaxy and assessed the galaxy sizes in order to estimate the column density associated with the host ISM. The comparison of the ISM column densities with the values measured from the X-ray spectral analysis pointed out that the contribution of the host ISM to the obscuration of the AGN emission can be substantial, ranging from ~10% up to ~100% of the value derived from the X-ray spectra. The absorption may occur at different physical scales in these sources and, in particular, the medium in the host galaxy is an ingredient that should be taken into account, since it may have a relevant role in driving the early co-evolution of galaxies with their black holes.