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Industry type and business size on economic growth: Comparing Australia's Regional and Metropolitan areas

- Mardaneh, Karim

  • Authors: Mardaneh, Karim
  • Date: 2011
  • Type: Text , Conference proceedings
  • Relation: 56th Annual ICSB World Conference; Back to the Future - Changes in Perspectives of Global Entrepreneurship and Innovation,Stockholm, Sweden, 15-18 June, 2011
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  • Description: While the main body of literature regarding small-to-medium enterprises is focused on formation and growth, there is insufficient research about the role of both (a) firm size and (b) location on economic growth. The role of firm size and industrial structure on economic growth has been examined by some researchers. Pagano (2003) and Pagano and Schivardi (2000) identified a positive association between average firm size and growth and Carree and Thurik (1999) found evidence that the low number of large firms in an industry could lead to a higher value added growth. The current study attempts to investigate the impact of industry structure and businesses operating within these industries on economic growth. This paper uses “k-means” clustering algorithm to cluster Statistical Local Areas. Regression analysis is utilised to identify drivers of economic growth. Preliminary results suggest that size of business may act as a driver of economic growth but the impact could vary based on location.

Machine learning in mental health: a scoping review of methods and applications

- Shatte, Adrian, Hutchinson, Delyse, Teague, Samantha

  • Authors: Shatte, Adrian , Hutchinson, Delyse , Teague, Samantha
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Psychological Medicine Vol. 49, no. 9 (2019), p. 1426-1448
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  • Description: This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis (ii) prognosis, treatment and support (iii) public health, and (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

A general stochastic clustering method for automatic cluster discovery

- Tan, Swee, Ting, Kaiming, Teng, Shyh

  • Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Pattern Recognition Vol. 44, no. 10-11 (2011), p. 2786-2799
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  • Description: Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the number of natural clusters hidden in the dataset. The problem is exacerbated when there is little or no additional information except the data itself. This paper proposes a general stochastic clustering method that is a simplification of nature-inspired ant-based clustering approach. It begins with a basic solution and then performs stochastic search to incrementally improve the solution until the underlying clusters emerge, resulting in automatic cluster discovery in datasets. This method differs from several recent methods in that it does not require users to input the number of clusters and it makes no explicit assumption about the underlying distribution of a dataset. Our experimental results show that the proposed method performs better than several existing methods in terms of clustering accuracy and efficiency in majority of the datasets used in this study. Our theoretical analysis shows that the proposed method has linear time and space complexities, and our empirical study shows that it can accurately and efficiently discover clusters in large datasets in which many existing methods fail to run.

Small businesses, Institutions, and Regional Incomes

- Mardaneh, Karim, O'Malley, Tony

  • Authors: Mardaneh, Karim , O'Malley, Tony
  • Date: 2014
  • Type: Text , Conference proceedings
  • Relation: 59th ISCB World Conference, Entrepreneurship and sustainability, Dublin, 11th June, 2014
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  • Description: Regional small businesses may rely on customers who earn income in local and global markets. Small business must transact with suppliers of knowledge and resources, transform those resources into innovative and saleable products or services, and transact with customers. Transformation, transaction and social activities, and the institutions which support them, are necessary for successful small businesses. Regional income and small businesses depend on innovation and trade provided by social and transaction institutions. In this paper we demonstrate this proposition empirically using a model and by investigating the relationship between regional income, transaction institutions, transformation institutions, and social institutions for 140 functional economic regions (FERs) in Australia. The model suggests that social institutions create a macro-environment in which transaction institutions and the transformation and trading activities of businesses can thrive, and help to generate regional income and prosperity. We follow others (Cooke et al., 2007) in arguing that strong transaction institutions are a necessary condition for regional innovation. Social institutions complement transaction institutions by providing education and training, arts and recreation, health care and social services. In the studies reported in this paper the capacity for search and intermediation of exchanges of all kinds (goods, services, knowledge etc.) is measured by the share of transaction institutions in regional employment. The capacity of social institutions is measured by the share of employment in social institutions. We argue that the market failures which cause regional failures to thrive may be made solvable by mobilising market making services to extend and provide governance for regional transactions with faraway markets.

THCluster: herb supplements categorization for precision traditional Chinese medicine

- Ruan, Chunyang, Wang, Ye, Zhang, Yanchun, Ma, Jiangang, Chen, Huijuan, Aickelin, Uwe, Zhu, Shanfeng, Zhang, Ting

  • Authors: Ruan, Chunyang , Wang, Ye , Zhang, Yanchun , Ma, Jiangang , Chen, Huijuan , Aickelin, Uwe , Zhu, Shanfeng , Zhang, Ting
  • Date: 2020
  • Type: Text , Conference proceedings
  • Relation: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);Kansas City, MO, USA; 13-16 Nov. 2017 p. 417-424
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  • Description: There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.

A CAD system using clustering and novel feature extraction technique

- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John

  • Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John
  • Date: 2005
  • Type: Text , Conference paper
  • Relation: Paper presented at CISTM 2005, Gurgaon, India : 24th - 26th July, 2005
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  • Description: Many previous efforts have utilized many different approaches for recognition in breast cancer detection using various ANN classifier-modelling techniques. Most of the previous work was concentred mostly on the classification of the damaged areas with the help of doctor’s suggestion. Doctors use to mark the suspicious areas area in the mammogram and the classifier only extract those marked areas and tries to classify it. An intelligent automatic diagnosis system can be very helpful for radiologist in diagnosing Breast cancer. In this research we are applying a local search gradient free clustering algorithm to find out the suspicious / damaged area. We compare our results with the doctor’s marking. Also it has been observed that, beyond a certain point, the inclusion of additional features leads to a worse rather than better performance. Moreover, the choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and a necessary number of samples. A common problem with the multi-category feature classification is the conflict between the categories. None of the feasible solutions allow simultaneous optimal solution for all categories. In order to find an optimal solution the search space can be divided based on an individual category in each sub region and finally merging them through decision spport system. Combining the feature selection with the classifier has been a major challenge for the researchers. A similar technique employed in both the levels often worsens their performance. Some preliminary studies has revealed that while using traditional canonical GA has been a good choice for feature selection modules, however under perform for the classifier level module. An evolutionary based algorithm for the classifier level provides a much better solution for this purpose. In this paper we propose a hybrid canonical based feature extraction technique with a combination of evolutionary algorithm based classifier using a feed forward MLP model.
  • Description: E1
  • Description: 2003001369

Statistical tests conducted with school environment data : The effect of teachers being clustered in schools

- Dorman, Jeffrey

  • Authors: Dorman, Jeffrey
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Learning Environments Research Vol. 12, no. 2 (2009), p. 85-99
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  • Description: This article discusses the effect of clustering on statistical tests conducted with school environment data. Because most school environment studies involve the collection of data from teachers nested within schools, the hierarchical nature to these data cannot be ignored. In particular, this article considers the influence of intraschool correlations on tests of statistical significance conducted with the individual teacher as the unit of analysis. Theory that adjusts t test scores for nested data in two-group comparisons is presented and applied to school environment data. This article demonstrates that Type I error rates inflate greatly as the intraschool correlation increases. Because data analysis techniques that recognise the clustering of teachers in schools are essential, it is recommended that either multilevel analysis or adjustments to statistical parameters be undertaken in school environment studies involving nested data.

A new solar power prediction method based on feature clustering and hybrid-classification-regression forecasting

- Nejati, Maryam, Amjady, Nima

  • Authors: Nejati, Maryam , Amjady, Nima
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE transactions on sustainable energy Vol. 13, no. 2 (2022), p. 1188-1198
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  • Description: Solar generation systems are globally extending in terms of scale and number, which highlights the increasing importance of solar power forecast. In this paper, a day-ahead solar power prediction method is proposed including 1) a novel feature selecting/clustering approach based on relevancy and redundancy criteria and 2) an innovative hybrid-classification-regression forecasting engine. The proposed feature selecting/clustering approach filters out irrelevant features and partitions relevant features to two separate subsets to decrease the redundancy of features. Each of these two subsets is separately trained by one forecasting engine and the final solar power prediction of the proposed method is obtained by a relevancy-based combination of these two forecasts. The proposed forecasting engine classifies the historical data based on the learnability of its constituent regression models and assigns each class of training samples to one regression model. Each regression model predicts the outputs of the test samples that belong to its class. The effectiveness of the proposed solar power prediction method is illustrated by testing on two real-world solar farms.

Application of rank correlation, clustering and classification in information security

- Beliakov, Gleb, Yearwood, John, Kelarev, Andrei

  • Authors: Beliakov, Gleb , Yearwood, John , Kelarev, Andrei
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Journal of Networks Vol. 7, no. 6 (2012), p. 935-945
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  • Description: This article is devoted to experimental investigation of a novel application of a clustering technique introduced by the authors recently in order to use robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on a particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, rank correlation is used to select a subset of features for dimensionality reduction. We investigate the effectiveness of the Pearson Linear Correlation Coefficient, the Spearman Rank Correlation Coefficient and the Goodman-Kruskal Correlation Coefficient in this application. Third, we use a consensus function to combine independent initial clusterings into one consensus clustering. Fourth, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms. © 2012 Academy Publisher.
  • Description: 2003010277

An application of novel clustering technique for information security

- Beliakov, Gleb, Yearwood, John, Kelarev, Andrei

  • Authors: Beliakov, Gleb , Yearwood, John , Kelarev, Andrei
  • Date: 2011
  • Type: Text , Conference paper
  • Relation: Applications and Techniques in Information Security Workshop p. 5-11
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  • Description: This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on the particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.
  • Description: 2003009195

Partial undersampling of imbalanced data for cyber threats detection

- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal

  • Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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  • Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
  • Description: E1

Rees matrix constructions for clustering of data

- Kelarev, Andrei, Watters, Paul, Yearwood, John

REPLOT : REtrieving Profile Links on Twitter for malicious campaign discovery

- Perez, Charles, Birregah, Babiga, Layton, Robert, Lemercier, Marc, Watters, Paul

  • Authors: Perez, Charles , Birregah, Babiga , Layton, Robert , Lemercier, Marc , Watters, Paul
  • Date: 2015
  • Type: Text , Journal article
  • Relation: AI Communications Vol. 29, no. 1 (2015), p. 107-122
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  • Description: Social networking sites are increasingly subject to malicious activities such as self-propagating worms, confidence scams and drive-by-download malwares. The high number of users associated with the presence of sensitive data, such as personal or professional information, is certainly an unprecedented opportunity for attackers. These attackers are moving away from previous platforms of attack, such as emails, towards social networking websites. In this paper, we present a full stack methodology for the identification of campaigns of malicious profiles on social networking sites, composed of maliciousness classification, campaign discovery and attack profiling. The methodology named REPLOT, for REtrieving Profile Links On Twitter, contains three major phases. First, profiles are analysed to determine whether they are more likely to be malicious or benign. Second, connections between suspected malicious profiles are retrieved using a late data fusion approach consisting of temporal and authorship analysis based models to discover campaigns. Third, the analysis of the discovered campaigns is performed to investigate the attacks. In this paper, we apply this methodology to a real world dataset, with a view to understanding the links between malicious profiles, their attack methods and their connections. Our analysis identifies a cluster of linked profiles focusing on propagating malicious links, as well as profiling two other major clusters of attacking campaigns. © 2016 - IOS Press and the authors. All rights reserved.

An overview of geospatial methods used in unintentional injury epidemiology

- Singh, Himalaya, Fortington, Lauren, Thompson, Helen, Finch, Caroline

  • Authors: Singh, Himalaya , Fortington, Lauren , Thompson, Helen , Finch, Caroline
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Injury Epidemiology Vol. 3, no. 32 (2016), p. 1-12
  • Relation: http://purl.org/au-research/grants/nhmrc/1058737
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  • Description: BACKGROUND: Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. METHODS: Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. RESULTS: From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). CONCLUSION: The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.

Influence of clustering on the opinion formation dynamics in online social networks

- Das, Rajkumar, Kamruzzaman, Joarder, Karmakar, Gour

Methods for global optimization of nonsmooth functions with applications

- Rubinov, Alex

  • Authors: Rubinov, Alex
  • Date: 2006
  • Type: Text , Journal article
  • Relation: Applied and Computational Mathematics Vol. 5, no. 1 (2006), p. 3-15
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  • Description: In this survey paper we present some results obtained in the Centre for Informatics and Applied Optimization (CIAO) at University of Ballarat, Australia, in the area of numerical global optimization. We describe a conceptual scheme of two methods developed in CIAO and present results of numerical experiments with some real world problems. The paper is based on a plenary lecture given by the author at the First International Conference on Control and Optimization with Industrial Applications, Baku, Azerbaijan, 2005.
  • Description: C1
  • Description: 2003001547

Establishing phishing provenance using orthographic features

- Liping, Ma, Yearwood, John, Watters, Paul

  • Authors: Liping, Ma , Yearwood, John , Watters, Paul
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at 2009 eCrime Researchers Summit, eCRIME '09, Tacoma, Washington : 20th-21st October 2009
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  • Description: After phishing message detection, determining the provenance of phishing messages and Websites is the second step to tracing cybercriminals. In this paper, we present a novel method to cluster phishing emails automatically using orthographic features. In particular, we develop an algorithm to cluster documents and remove redundant features at the same time. After collecting all the possible features based on observation, we adapt the modified global k-mean method repeatedly, and generate the objective function values over a range of tolerance values across different subsets of features. Finally, we identify the appropriate clusters based on studying the distribution of the objective function values. Experimental evaluation of a large number of computations demonstrates that our clustering and feature selection techniques are highly effective and achieve reliable results.
  • Description: 2003007842

Subgraph adaptive structure-aware graph contrastive learning

- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng

  • Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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  • Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.

A new modification of Kohonen neural network for VQ and clustering problems

- Mohebi, Ehsan, Bagirov, Adil

  • Authors: Mohebi, Ehsan , Bagirov, Adil
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Proceedings of the 11-th Australasian Data Mining Conference (AusDM'13) Vol. 146, p. 81-88
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  • Description: Vector Quantization (VQ) and Clustering are significantly important in the field of data mining and pattern recognition. The Self Organizing Maps has been widely used for clustering and topology visualization. The topology of the SOM and its initial neurons play an important role in the convergence of the Kohonen neural network. In this paper, in order to improve the convergence of the SOM we introduce an algorithm based on the split and merging of clusters to initialize neurons. We also introduce a topology based on this initialization to optimize the vector quantization error. Such an approach allows one to find global or near global solution to the vector quantization and consequently clustering problem. The numerical results on 4 small to large real-world data sets are reported to demonstrate the performance of the proposed algorithm.

Two level clustering using SOM and dynamical systems

- Ghosh, Ranadhir, Mammadov, Musa, Ghosh, Moumita, Yearwood, John