An algorithm for minimizing clustering functions
- Authors: Bagirov, Adil , Ugon, Julien
- Date: 2005
- Type: Text , Journal article
- Relation: Optimization Vol. 54, no. 4-5 (Aug-Oct 2005), p. 351-368
- Full Text:
- Reviewed:
- Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An algorithm for solving the latter optimization problem is developed which allows one to significantly reduce the computational efforts. This algorithm is based on the so-called discrete gradient method. Results of numerical experiments are presented which demonstrate the effectiveness of the proposed algorithm.
- Description: C1
- Description: 2003001266
- Authors: Bagirov, Adil , Ugon, Julien
- Date: 2005
- Type: Text , Journal article
- Relation: Optimization Vol. 54, no. 4-5 (Aug-Oct 2005), p. 351-368
- Full Text:
- Reviewed:
- Description: The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An algorithm for solving the latter optimization problem is developed which allows one to significantly reduce the computational efforts. This algorithm is based on the so-called discrete gradient method. Results of numerical experiments are presented which demonstrate the effectiveness of the proposed algorithm.
- Description: C1
- Description: 2003001266
Optimization based methods for solving some problems in telecommunications and the internet
- Authors: Jia, Long
- Date: 2005
- Type: Text , Thesis , PhD
- Full Text:
- Description: The purpose of this thesis is to develop some new algorithms based on optimization techniques for solving some problems in some areas of telecommunications and the Internet. There are two main parts to this thesis. In the first part we discuss optimization based stochastic and queueing models in telecommunications network corrective maintenance. In the second part we develop optimization based clustering (OBC) algorithms for network evolution and multicast routing. The most typical scenario encountered during mathematical optimization modelling in telecommunications, for example, is to minimize the cost of establishment and maintenance of the networks subject to the performance constraints of the networks and the reliability constraints of the networks as well. Most of these optimization problems are global optimization, that is, they have many local minima and most of these local minima do not provide any useful information for solving these problems. Therefore, the development of effective methods for solving such global optimization problems is important. To run the telecommunications networks with cost-effective network maintenance,we need to establish a practical maintenance model and optimize it. In the first part of the thesis, we solve a known stochastic programming maintenance optimization model with a direct method and then develop some new models. After that we introduce queue programming models in telecommunications network maintenance optimization. The ideas of profit, loss, and penalty will help telecommunications companies have a good view of their maintenance policies and help them improve their service. In the second part of this thesis we propose the use of optimization based clustering (OBC) algorithms to determine level-constrained hierarchical trees for network evolution and multicast routing. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artifiicial and real-world databases are reported.
- Description: Doctor of Philosophy
- Authors: Jia, Long
- Date: 2005
- Type: Text , Thesis , PhD
- Full Text:
- Description: The purpose of this thesis is to develop some new algorithms based on optimization techniques for solving some problems in some areas of telecommunications and the Internet. There are two main parts to this thesis. In the first part we discuss optimization based stochastic and queueing models in telecommunications network corrective maintenance. In the second part we develop optimization based clustering (OBC) algorithms for network evolution and multicast routing. The most typical scenario encountered during mathematical optimization modelling in telecommunications, for example, is to minimize the cost of establishment and maintenance of the networks subject to the performance constraints of the networks and the reliability constraints of the networks as well. Most of these optimization problems are global optimization, that is, they have many local minima and most of these local minima do not provide any useful information for solving these problems. Therefore, the development of effective methods for solving such global optimization problems is important. To run the telecommunications networks with cost-effective network maintenance,we need to establish a practical maintenance model and optimize it. In the first part of the thesis, we solve a known stochastic programming maintenance optimization model with a direct method and then develop some new models. After that we introduce queue programming models in telecommunications network maintenance optimization. The ideas of profit, loss, and penalty will help telecommunications companies have a good view of their maintenance policies and help them improve their service. In the second part of this thesis we propose the use of optimization based clustering (OBC) algorithms to determine level-constrained hierarchical trees for network evolution and multicast routing. This problem is formulated as an optimization problem with a non-smooth, non-convex objective function. Different algorithms are examined for solving this problem. Results of numerical experiments using some artifiicial and real-world databases are reported.
- Description: Doctor of Philosophy
Regional clusters : Classification and overlap of wine and tourism micro-clusters
- Authors: McRae-Williams, Pamela
- Date: 2007
- Type: Text , Book chapter
- Relation: Small business clustering technologies : applications in marketing, management, IT and economics Chapter p. 141-159
- Full Text:
- Reviewed:
- Description: "This book examines the development and role of small business clusters from a variety of disciplines : economics, marketing, management and information systems. It gathers perspectives from varied disciplines and countries, extending current knowledge by emphasizing the pressure that international competition and IT have on the geographic structure of clusters, possibly leading to a new 'a-spatial' option developing"--Provided by publisher.
- Description: B1
- Description: 2003005239
- Authors: McRae-Williams, Pamela
- Date: 2007
- Type: Text , Book chapter
- Relation: Small business clustering technologies : applications in marketing, management, IT and economics Chapter p. 141-159
- Full Text:
- Reviewed:
- Description: "This book examines the development and role of small business clusters from a variety of disciplines : economics, marketing, management and information systems. It gathers perspectives from varied disciplines and countries, extending current knowledge by emphasizing the pressure that international competition and IT have on the geographic structure of clusters, possibly leading to a new 'a-spatial' option developing"--Provided by publisher.
- Description: B1
- Description: 2003005239
Adaptive clustering with feature ranking for DDoS attacks detection
- Zi, Lifang, Yearwood, John, Wu, Xin
- Authors: Zi, Lifang , Yearwood, John , Wu, Xin
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE.
- Authors: Zi, Lifang , Yearwood, John , Wu, Xin
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE.
Determining provenance in phishing websites using automated conceptual analysis
- Layton, Robert, Watters, Paul
- Authors: Layton, Robert , Watters, Paul
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 eCrime Researchers Summit, eCRIME '09, Tacoma, Washington : 20th-21st October 2009 p. 1-7
- Full Text:
- Description: Phishing is a form of online fraud with drastic consequences for the victims and institutions being defrauded. A phishing attack tries to create a believable environment for the intended victim to enter their confidential data such that the attacker can use or sell this information later. In order to apprehend phishers, law enforcement agencies need automated systems capable of tracking the size and scope of phishing attacks, in order to more wisely use their resources shutting down the major players, rather then wasting resources stopping smaller operations. In order to develop these systems, phishing attacks need to be clustered by provenance in a way that adequately profiles these evolving attackers. The research presented in this paper looks at the viability of using automated conceptual analysis through cluster analysis techniques on phishing websites, with the aim of determining provenance of these phishing attacks. Conceptual analysis is performed on the source code of the websites, rather than the final text that is displayed to the user, eliminating problems with rendering obfuscation and increasing the distinctiveness brought about by differences in coding styles of the phishers. By using cluster analysis algorithms, distinguishing factors between groups of phishing websites can be obtained. The results indicate that it is difficult to separate websites by provenance without also separating by intent, by looking at the phishing websites alone. Instead, the methods discussed in this paper should form part of a larger system that uses more information about the phishing attacks.
- Authors: Layton, Robert , Watters, Paul
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at 2009 eCrime Researchers Summit, eCRIME '09, Tacoma, Washington : 20th-21st October 2009 p. 1-7
- Full Text:
- Description: Phishing is a form of online fraud with drastic consequences for the victims and institutions being defrauded. A phishing attack tries to create a believable environment for the intended victim to enter their confidential data such that the attacker can use or sell this information later. In order to apprehend phishers, law enforcement agencies need automated systems capable of tracking the size and scope of phishing attacks, in order to more wisely use their resources shutting down the major players, rather then wasting resources stopping smaller operations. In order to develop these systems, phishing attacks need to be clustered by provenance in a way that adequately profiles these evolving attackers. The research presented in this paper looks at the viability of using automated conceptual analysis through cluster analysis techniques on phishing websites, with the aim of determining provenance of these phishing attacks. Conceptual analysis is performed on the source code of the websites, rather than the final text that is displayed to the user, eliminating problems with rendering obfuscation and increasing the distinctiveness brought about by differences in coding styles of the phishers. By using cluster analysis algorithms, distinguishing factors between groups of phishing websites can be obtained. The results indicate that it is difficult to separate websites by provenance without also separating by intent, by looking at the phishing websites alone. Instead, the methods discussed in this paper should form part of a larger system that uses more information about the phishing attacks.
Cluster based rule discovery model for enhancement of government's tobacco control strategy
- Huda, Shamsul, Yearwood, John, Borland, Ron
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Discovery of interesting rules describing the behavioural patterns of smokers' quitting intentions is an important task in the determination of an effective tobacco control strategy. In this paper, we investigate a compact and simplified rule discovery process for predicting smokers' quitting behaviour that can provide feedback to build an scientific evidence-based adaptive tobacco control policy. Standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space which are orthogonal to the axis of the feature of a particular decision node. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster based rule discovery model (CRDM) for generation of more compact and simplified rules for the enhancement of tobacco control policy. The clusterbased approach builds conceptual groups from which a set of decision trees (a decision forest) are constructed. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers' quitting intention more accurately than a single decision tree. © 2010 IEEE.
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Discovery of interesting rules describing the behavioural patterns of smokers' quitting intentions is an important task in the determination of an effective tobacco control strategy. In this paper, we investigate a compact and simplified rule discovery process for predicting smokers' quitting behaviour that can provide feedback to build an scientific evidence-based adaptive tobacco control policy. Standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space which are orthogonal to the axis of the feature of a particular decision node. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster based rule discovery model (CRDM) for generation of more compact and simplified rules for the enhancement of tobacco control policy. The clusterbased approach builds conceptual groups from which a set of decision trees (a decision forest) are constructed. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers' quitting intention more accurately than a single decision tree. © 2010 IEEE.
Derivative free algorithms for nonsmooth and global optimization with application in cluster analysis
- Authors: Ganjehlou, Asef Nazari
- Date: 2009
- Type: Text , Thesis , PhD
- Full Text:
- Description: This thesis is devoted to the development of algorithms for solving nonsmooth nonconvex problems. Some of these algorithms are derivative free methods.
- Description: Doctor of Philosophy
- Authors: Ganjehlou, Asef Nazari
- Date: 2009
- Type: Text , Thesis , PhD
- Full Text:
- Description: This thesis is devoted to the development of algorithms for solving nonsmooth nonconvex problems. Some of these algorithms are derivative free methods.
- Description: Doctor of Philosophy
Evaluating authorship distance methods using the positive Silhouette coefficient
- Layton, Robert, Watters, Paul, Dazeley, Richard
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 4 (2013), p. 517-535
- Full Text:
- Reviewed:
- Description: Unsupervised Authorship Analysis (UAA) aims to cluster documents by authorship without knowing the authorship of any documents. An important factor in UAA is the method for calculating the distance between documents. This choice of the authorship distance method is considered more critical to the end result than the choice of cluster analysis algorithm. One method for measuring the correlation between a distance metric and a labelling (such as class values or clusters) is the Silhouette Coefficient (SC). The SC can be leveraged by measuring the correlation between the authorship distance method and the true authorship, evaluating the quality of the distance method. However, we show that the SC can be severely affected by outliers. To address this issue, we introduce the Positive Silhouette Coefficient, given as the proportion of instances with a positive SC value. This metric is not easily altered by outliers and produces a more robust metric. A large number of authorship distance methods are then compared using the PSC, and the findings are presented. This research provides an insight into the efficacy of methods for UAA and presents a framework for testing authorship distance methods.
- Description: C1
- Authors: Layton, Robert , Watters, Paul , Dazeley, Richard
- Date: 2013
- Type: Text , Journal article
- Relation: Natural Language Engineering Vol. 19, no. 4 (2013), p. 517-535
- Full Text:
- Reviewed:
- Description: Unsupervised Authorship Analysis (UAA) aims to cluster documents by authorship without knowing the authorship of any documents. An important factor in UAA is the method for calculating the distance between documents. This choice of the authorship distance method is considered more critical to the end result than the choice of cluster analysis algorithm. One method for measuring the correlation between a distance metric and a labelling (such as class values or clusters) is the Silhouette Coefficient (SC). The SC can be leveraged by measuring the correlation between the authorship distance method and the true authorship, evaluating the quality of the distance method. However, we show that the SC can be severely affected by outliers. To address this issue, we introduce the Positive Silhouette Coefficient, given as the proportion of instances with a positive SC value. This metric is not easily altered by outliers and produces a more robust metric. A large number of authorship distance methods are then compared using the PSC, and the findings are presented. This research provides an insight into the efficacy of methods for UAA and presents a framework for testing authorship distance methods.
- Description: C1
Patterns of comorbidity in community-dwelling older people hospitalised for fall-related injury: A cluster analysis
- Vu, Trang, Finch, Caroline, Day, Lesley
- Authors: Vu, Trang , Finch, Caroline , Day, Lesley
- Date: 2011
- Type: Text , Journal article
- Relation: BMC Geriatrics Vol. 11, no. 45 (2011), p. 1-10
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Background: Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. Methods: We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity) we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. Results: More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7). The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. Conclusions: The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients should be investigated by future studies. Our findings have particular relevance for falls prevention strategies, clinical practice and planning of follow-up services for these patients.
- Authors: Vu, Trang , Finch, Caroline , Day, Lesley
- Date: 2011
- Type: Text , Journal article
- Relation: BMC Geriatrics Vol. 11, no. 45 (2011), p. 1-10
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Background: Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. Methods: We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity) we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. Results: More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7). The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. Conclusions: The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients should be investigated by future studies. Our findings have particular relevance for falls prevention strategies, clinical practice and planning of follow-up services for these patients.
Nonsmooth optimization models and algorithms for data clustering and visualization
- Authors: Mohebi, Ehsan
- Date: 2015
- Type: Text , Thesis
- Full Text:
- Description: Cluster analysis deals with the problem of organization of a collection of patterns into clusters based on a similarity measure. Various distance functions can be used to define this measure. Clustering problems with the similarity measure defined by the squared Euclidean distance have been studied extensively over the last five decades. However, problems with other Minkowski norms have attracted significantly less attention. The use of different similarity measures may help to identify different cluster structures of a data set. This in turn may help to significantly improve the decision making process. High dimensional data visualization is another important task in the field of data mining and pattern recognition. To date, the principal component analysis and the self-organizing maps techniques have been used to solve such problems. In this thesis we develop algorithms for solving clustering problems in large data sets using various similarity measures. Such similarity measures are based on the squared L
- Description: Doctor of Philosophy
- Authors: Mohebi, Ehsan
- Date: 2015
- Type: Text , Thesis
- Full Text:
- Description: Cluster analysis deals with the problem of organization of a collection of patterns into clusters based on a similarity measure. Various distance functions can be used to define this measure. Clustering problems with the similarity measure defined by the squared Euclidean distance have been studied extensively over the last five decades. However, problems with other Minkowski norms have attracted significantly less attention. The use of different similarity measures may help to identify different cluster structures of a data set. This in turn may help to significantly improve the decision making process. High dimensional data visualization is another important task in the field of data mining and pattern recognition. To date, the principal component analysis and the self-organizing maps techniques have been used to solve such problems. In this thesis we develop algorithms for solving clustering problems in large data sets using various similarity measures. Such similarity measures are based on the squared L
- Description: Doctor of Philosophy
Data classification through nonsmooth optimization
- Authors: Soukhoroukova, Nadejda
- Date: 2003
- Type: Text , Thesis , PhD
- Full Text:
- Description: "The purpose of this thesis is to develop and test new methods for data classification based on mathematical programming and nonsmooth optimization."
- Description: Doctor of Philosophy
- Authors: Soukhoroukova, Nadejda
- Date: 2003
- Type: Text , Thesis , PhD
- Full Text:
- Description: "The purpose of this thesis is to develop and test new methods for data classification based on mathematical programming and nonsmooth optimization."
- Description: Doctor of Philosophy
Spatio-temporal epidemiology of the cholera outbreak in Papua New Guinea, 2009-2011
- Horwood, Paul, Karl, Stephan, Mueller, Ivo, Jonduo, Marinjho, Pavlin, Boris, Dagina, Rosheila, Ropa, Berry, Bieb, Sibauk, Rosewell, Alexander, Umezaki, Masahiro, Siba, Peter, Greenhill, Andrew
- Authors: Horwood, Paul , Karl, Stephan , Mueller, Ivo , Jonduo, Marinjho , Pavlin, Boris , Dagina, Rosheila , Ropa, Berry , Bieb, Sibauk , Rosewell, Alexander , Umezaki, Masahiro , Siba, Peter , Greenhill, Andrew
- Date: 2014
- Type: Text , Journal article
- Relation: BMC Infectious Diseases Vol. 14, no. 1 (2014), p.
- Full Text:
- Reviewed:
- Description: Background: Cholera continues to be a devastating disease in many developing countries where inadequate safe water supply and poor sanitation facilitate spread. From July 2009 until late 2011 Papua New Guinea experienced the first outbreak of cholera recorded in the country, resulting in > 15,500 cases and > 500 deaths. Methods: Using the national cholera database, we analysed the spatio-temporal distribution and clustering of the Papua New Guinea cholera outbreak. The Kulldorff space-time permutation scan statistic, contained in the software package SatScan v9.2 was used to describe the first 8 weeks of the outbreak in Morobe Province before cholera cases spread throughout other regions of the country. Data were aggregated at the provincial level to describe the spread of the disease to other affected provinces. Results: Spatio-temporal and cluster analyses revealed that the outbreak was characterized by three distinct phases punctuated by explosive propagation of cases when the outbreak spread to a new region. The lack of road networks across most of Papua New Guinea is likely to have had a major influence on the slow spread of the disease during this outbreak. Conclusions: Identification of high risk areas and the likely mode of spread can guide government health authorities to formulate public health strategies to mitigate the spread of the disease through education campaigns, vaccination, increased surveillance in targeted areas and interventions to improve water, sanitation and hygiene.
- Authors: Horwood, Paul , Karl, Stephan , Mueller, Ivo , Jonduo, Marinjho , Pavlin, Boris , Dagina, Rosheila , Ropa, Berry , Bieb, Sibauk , Rosewell, Alexander , Umezaki, Masahiro , Siba, Peter , Greenhill, Andrew
- Date: 2014
- Type: Text , Journal article
- Relation: BMC Infectious Diseases Vol. 14, no. 1 (2014), p.
- Full Text:
- Reviewed:
- Description: Background: Cholera continues to be a devastating disease in many developing countries where inadequate safe water supply and poor sanitation facilitate spread. From July 2009 until late 2011 Papua New Guinea experienced the first outbreak of cholera recorded in the country, resulting in > 15,500 cases and > 500 deaths. Methods: Using the national cholera database, we analysed the spatio-temporal distribution and clustering of the Papua New Guinea cholera outbreak. The Kulldorff space-time permutation scan statistic, contained in the software package SatScan v9.2 was used to describe the first 8 weeks of the outbreak in Morobe Province before cholera cases spread throughout other regions of the country. Data were aggregated at the provincial level to describe the spread of the disease to other affected provinces. Results: Spatio-temporal and cluster analyses revealed that the outbreak was characterized by three distinct phases punctuated by explosive propagation of cases when the outbreak spread to a new region. The lack of road networks across most of Papua New Guinea is likely to have had a major influence on the slow spread of the disease during this outbreak. Conclusions: Identification of high risk areas and the likely mode of spread can guide government health authorities to formulate public health strategies to mitigate the spread of the disease through education campaigns, vaccination, increased surveillance in targeted areas and interventions to improve water, sanitation and hygiene.
Cyberattack triage using incremental clustering for intrusion detection systems
- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
Attitude and achievement of first-year chemistry undergraduate students at the university of the South Pacific
- Johnson, Joel, Reddy, Pritika, Sharma, Sushita, Wakeling, Lara, Mani, Janice, Benveniste, Tessa, Naiker, Mani, Brown, Stephen
- Authors: Johnson, Joel , Reddy, Pritika , Sharma, Sushita , Wakeling, Lara , Mani, Janice , Benveniste, Tessa , Naiker, Mani , Brown, Stephen
- Date: 2022
- Type: Text , Journal article
- Relation: Frontiers in Education Vol. 7, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Student attitude toward chemistry may influence engagement and achievement in chemistry-related courses, however, equivocal results in studies conducted in Western countries to date indicate this relationship requires further investigation. In this study, we investigated the correlation between attitude toward chemistry and achievement amongst a cohort of first-year undergraduate students from The University of the South Pacific (USP). A cluster analysis was used to identify low- and high-achieving groups of students to further explore potential correlations. There was a positive correlation between the cognitive and affective components of attitude among low-achieving students, but not among high-achieving students. The cognitive component of attitude did not appear to be strongly correlated with achievement in students from either group, although the affective component was positively correlated with achievement. The single item most strongly correlated with student achievement was their response on the Worthless-Beneficial scale. One of the notable findings was the differences in the attitude-achievement relationship between low-achieving and high-achieving students, suggesting that combining these clusters of students into a single group for analysis may obscure underlying correlations. Chemistry educators should continue to target their teaching styles to cater to different learning styles and achievement levels of students, including cognitive and non-cognitive learning styles. Copyright © 2022 Johnson, Reddy, Sharma, Wakeling, Mani, Benveniste, Naiker and Brown.
- Authors: Johnson, Joel , Reddy, Pritika , Sharma, Sushita , Wakeling, Lara , Mani, Janice , Benveniste, Tessa , Naiker, Mani , Brown, Stephen
- Date: 2022
- Type: Text , Journal article
- Relation: Frontiers in Education Vol. 7, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Student attitude toward chemistry may influence engagement and achievement in chemistry-related courses, however, equivocal results in studies conducted in Western countries to date indicate this relationship requires further investigation. In this study, we investigated the correlation between attitude toward chemistry and achievement amongst a cohort of first-year undergraduate students from The University of the South Pacific (USP). A cluster analysis was used to identify low- and high-achieving groups of students to further explore potential correlations. There was a positive correlation between the cognitive and affective components of attitude among low-achieving students, but not among high-achieving students. The cognitive component of attitude did not appear to be strongly correlated with achievement in students from either group, although the affective component was positively correlated with achievement. The single item most strongly correlated with student achievement was their response on the Worthless-Beneficial scale. One of the notable findings was the differences in the attitude-achievement relationship between low-achieving and high-achieving students, suggesting that combining these clusters of students into a single group for analysis may obscure underlying correlations. Chemistry educators should continue to target their teaching styles to cater to different learning styles and achievement levels of students, including cognitive and non-cognitive learning styles. Copyright © 2022 Johnson, Reddy, Sharma, Wakeling, Mani, Benveniste, Naiker and Brown.
- «
- ‹
- 1
- ›
- »