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Visual tools for analysing evolution, emergence, and error in data streams

- Hart, Sol, Yearwood, John, Bagirov, Adil

  • Authors: Hart, Sol , Yearwood, John , Bagirov, Adil
  • Date: 2007
  • Type: Text , Conference paper
  • Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 987-992
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  • Description: The relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. Through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.
  • Description: 2003005432

Classification for accuracy and insight : A weighted sum approach

- Quinn, Anthony, Stranieri, Andrew, Yearwood, John

  • Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John
  • Date: 2007
  • Type: Text , Conference paper
  • Relation: Paper presented at Sixth Australasian Data Mining Conference, AusDM 2007, Gold Coast, Queensland, Victoria : 3rd-4th December 2007 p. 203-208
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  • Description: This research presents a classifier that aims to provide insight into a dataset in addition to achieving classification accuracies comparable to other algorithms. The classifier called, Automated Weighted Sum (AWSum) uses a weighted sum approach where feature values are assigned weights that are summed and compared to a threshold in order to classify an example. Though naive, this approach is scalable, achieves accurate classifications on standard datasets and also provides a degree of insight. By insight we mean that the technique provides an appreciation of the influence a feature value has on class values, relative to each other. AWSum provides a focus on the feature value space that allows the technique to identify feature values and combinations of feature values that are sensitive and important for a classification. This is particularly useful in fields such as medicine where this sort of micro-focus and understanding is critical in classification.
  • Description: 2003005504

Dynamical systems based on a fuzzy derivative and its applications to data classification

- Mammadov, Musa, Rubinov, Alex, Yearwood, John

Rees matrix constructions for clustering of data

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

RBACS : Rootkit behavioral analysis and classification system

- Lobo, Desmond, Watters, Paul, Wu, Xinwen

  • Authors: Lobo, Desmond , Watters, Paul , Wu, Xinwen
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Paper presented at 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010, Phuket : 9th-10th January 2010 p. 75-80
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  • Description: In this paper, we focus on rootkits, a special type of malicious software (malware) that operates in an obfuscated and stealthy mode to evade detection. Categorizing these rootkits will help in detecting future attacks against the business community. We first developed a theoretical framework for classifying rootkits. Based on our theoretical framework, we then proposed a new rootkit classification system and tested our system on a sample of rootkits that use inline function hooking. Our experimental results showed that our system could successfully categorize the sample using unsupervised clustering. © 2010 IEEE.

A classification algorithm that derives weighted sum scores for insight into disease

- Quinn, Anthony, Stranieri, Andrew, Yearwood, John, Hafen, Gaudenz

  • Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand : Vol. 97, p. 13-17
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  • Description: Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

- Saunders, Gary

  • Authors: Saunders, Gary
  • Date: 2006
  • Type: Text , Thesis , PhD
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  • Description: The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.
  • Description: Doctor of Philosophy

Identifying rootkit infections using data mining

- Lobo, Desmond, Watters, Paul, Wu, Xinwen

  • Authors: Lobo, Desmond , Watters, Paul , Wu, Xinwen
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Paper presented at 2010 International Conference on Information Science and Applications, ICISA 2010, Seoul, Korea : p. 1-7
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  • Description: Rootkits refer to software that is used to hide the presence and activity of malware and permit an attacker to take control of a computer system. In our previous work, we focused strictly on identifying rootkits that use inline function hooking techniques to remain hidden. In this paper, we extend our previous work by including rootkits that use other types of hooking techniques, such as those that hook the IATs (Import Address Tables) and SSDTs (System Service Descriptor Tables). Unlike other malware identification techniques, our approach involved conducting dynamic analyses of various rootkits and then determining the family of each rootkit based on the hooks that had been created on the system. We demonstrated the effectiveness of this approach by first using the CLOPE (Clustering with sLOPE) algorithm to cluster a sample of rootkits into several families; next, the ID3 (Iterative Dichotomiser 3) algorithm was utilized to generate a decision tree for identifying the rootkit that had infected a machine. ©2010 IEEE.

An efficient algorithm for the incremental construction of a piecewise linear classifier

- Bagirov, Adil, Ugon, Julien, Webb, Dean

  • Authors: Bagirov, Adil , Ugon, Julien , Webb, Dean
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Information Systems Vol. 36, no. 4 (2011), p. 782-790
  • Relation: http://purl.org/au-research/grants/arc/DP0666061
  • Full Text: false
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  • Description: In this paper the problem of finding piecewise linear boundaries between sets is considered and is applied for solving supervised data classification problems. An algorithm for the computation of piecewise linear boundaries, consisting of two main steps, is proposed. In the first step sets are approximated by hyperboxes to find so-called "indeterminate" regions between sets. In the second step sets are separated inside these "indeterminate" regions by piecewise linear functions. These functions are computed incrementally starting with a linear function. Results of numerical experiments are reported. These results demonstrate that the new algorithm requires a reasonable training time and it produces consistently good test set accuracy on most data sets comparing with mainstream classifiers. © 2010 Elsevier B.V. All rights reserved.

Classes and clusters in data analysis

- Rubinov, Alex, Sukhorukova, Nadezda, Ugon, Julien

  • Authors: Rubinov, Alex , Sukhorukova, Nadezda , Ugon, Julien
  • Date: 2006
  • Type: Text , Journal article
  • Relation: European Journal of Operational Research Vol. 173, no. 3 (Sep 2006), p. 849-865
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  • Description: We discuss the relation between classes and clusters in datasets with given classes. We examine the distribution of classes within obtained clusters, using different clustering methods which are based on different techniques. We also study the structure of the obtained clusters. One of the main conclusions, obtained in this research is that the notion purity cannot be always used for evaluation of accuracy of clustering techniques. (c) 2005 Elsevier B.V. All rights reserved.
  • Description: C1
  • Description: 2003001593

From convex to nonconvex: A loss function analysis for binary classification

- Zhao, Lei, Mammadov, Musa, Yearwood, John

  • Authors: Zhao, Lei , Mammadov, Musa , Yearwood, John
  • Date: 2010
  • Type: Text , Conference paper
  • Relation: Paper presented at10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 p. 1281-1288
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  • Description: Problems of data classification can be studied in the framework of regularization theory as ill-posed problems. In this framework, loss functions play an important role in the application of regularization theory to classification. In this paper, we review some important convex loss functions, including hinge loss, square loss, modified square loss, exponential loss, logistic regression loss, as well as some non-convex loss functions, such as sigmoid loss, ø-loss, ramp loss, normalized sigmoid loss, and the loss function of 2 layer neural network. Based on the analysis of these loss functions, we propose a new differentiable non-convex loss function, called smoothed 0-1 loss function, which is a natural approximation of the 0-1 loss function. To compare the performance of different loss functions, we propose two binary classification algorithms for binary classification, one for convex loss functions, the other for non-convex loss functions. A set of experiments are launched on several binary data sets from the UCI repository. The results show that the proposed smoothed 0-1 loss function is robust, especially for those noisy data sets with many outliers. © 2010 IEEE.

Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules : Application to prostate cancer patients

- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael

  • Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
  • Date: 2005
  • Type: Text , Journal article
  • Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
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  • Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
  • Description: C1
  • Description: 2003001265

An algorithm for the optimization of multiple classifers in data mining based on graphs

- Kelarev, Andrei, Ryan, Joe, Yearwood, John

  • Authors: Kelarev, Andrei , Ryan, Joe , Yearwood, John
  • Date: 2009
  • Type: Text , Journal article
  • Relation: The Journal of Combinatorial Mathematics and Combinatorial Computing Vol. 71, no. (2009), p. 65-85
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  • Description: This article develops an efficient combinatorial algorithm based on labeled directed graphs and motivated by applications in data mining for designing multiple classifiers. Our method originates from the standard approach described in [37]. It defines a representation of a multiclass classifier in terms of several binary classifiers. We are using labeled graphs to introduce additional structure on the classifier. Representations of this sort are known to have serious advantages. An important property of these representations is their ability to correct errors of individual binary classifiers and produce correct combined output. For every representation like this we develop a combinatorial algorithm with quadratic running time to compute the largest number of errors of individual binary classifiers which can be corrected by the combined multiple classifier. In addition, we consider the question of optimizing the classifiers of this type and find all optimal representations for these multiple classifiers.
  • Description: 2003007563

Optimization of classifiers for data mining based on combinatorial semigroups

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

  • Authors: Kelarev, Andrei , Yearwood, John , Watters, Paul
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Semigroup Forum Vol. 82, no. 2 (2011), p. 1-10
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  • Description: The aim of the present article is to obtain a theoretical result essential for applications of combinatorial semigroups for the design of multiple classification systems in data mining. We consider a novel construction of multiple classification systems, or classifiers, combining several binary classifiers. The construction is based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrix. Our main theorem gives a complete description of all optimal classifiers in this novel construction. © 2011 Springer Science+Business Media, LLC.

AWSum - applying data mining in a health care scenario

- Quinn, Anthony, Jelinek, Herbert, Stranieri, Andrew, Yearwood, John

  • Authors: Quinn, Anthony , Jelinek, Herbert , Stranieri, Andrew , Yearwood, John
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Paper presented at International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, Sydney, New South Wales : 15th-18th December 2008 p. 291-296
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  • Description: This paper investigates the application of a new data mining algorithm called Automated Weighted Sum, (AWSum), to diabetes screening data to explore its use in providing researchers with new insight into the disease and secondarily to explore the potential the algorithm has for the generation of prognostic models for clinical use. There are many data mining classifiers that produce high levels of predictive accuracy but their application to health research and clinical applications is limited because they are complex, produce results that are difficult to interpret and are difficult to integrate with current knowledge and practises. This is because most focus on accuracy at the expense of informing the user as to the influences that lead to their classification results. By providing this information on influences a researcher can be pointed to new potentially interesting avenues for investigation. AWSum measures influence by calculating a weight for each feature value that represents its influence on a class value relative to other class values. The results produced, although on limited data, indicated the approach has potential uses for research and has some characteristics that may be useful in the future development of prognostic models.
  • Description: 2003006660

Derivative-free hybrid methods in global optimization and their applications

- Zhang, Jiapu

  • Authors: Zhang, Jiapu
  • Date: 2005
  • Type: Text , Thesis , PhD
  • Full Text:
  • Description: In recent years large-scale global optimization (GO) problems have drawn considerable attention. These problems have many applications, in particular in data mining and biochemistry. Numerical methods for GO are often very time consuming and could not be applied for high-dimensional non-convex and / or non-smooth optimization problems. The thesis explores reasons why we need to develop and study new algorithms for solving large-scale GO problems .... The thesis presents several derivative-free hybrid methods for large scale GO problems. These methods do not guarantee the calculation of a global solution; however, results of numerical experiments presented in this thesis demonstrate that they, as a rule, calculate a solution which is a global one or close to it. Their applications to data mining problems and the protein folding problem are demonstrated.
  • Description: Doctor of Philosophy

A formula for multiple classifiers in data mining based on Brandt semigroups

- Kelarev, Andrei, Yearwood, John, Mammadov, Musa

  • Authors: Kelarev, Andrei , Yearwood, John , Mammadov, Musa
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Semigroup Forum Vol. 78, no. 2 (2009), p. 293-309
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  • Description: A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect. 7.5). The aim of this paper is to investigate representations of this sort based on Brandt semigroups. We give a formula for the maximum number of errors of binary classifiers, which can be corrected by a multiple classifier of this type. Examples show that our formula does not carry over to larger classes of semigroups. © 2008 Springer Science+Business Media, LLC.

Optimization in data mining

- Karasozen, Bulent, Rubinov, Alex, Weber, Gerhard-Wilhelm

Determining the influence of visual training on EEG activity patterns using association rule mining

- Yan, Fangang, Watters, Paul, Wang, Wei

  • Authors: Yan, Fangang , Watters, Paul , Wang, Wei
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: To confirm that visual training can change EEG patterns by association rule mining method, firstly, we collected the EEG of people who are under a long-term visual professional training (visual training group) and novice people (control group) during a specific mental tasks. Secondly, we determined the difference of brain electrical activity between the two groups using machine learning methods. Thirdly, we discovered distinct patterns using association rule algorithm, finding that the two groups were separable based on their completion of visual professional cognitive tasks. In the beta band, visual training group showed a specific and significant association pattern which included FP1 and C4. The results indicate that the EEG patterns were modified because of visual professional training. We further discuss the impact of long-term visual professional training on the EEG. © 2011 IEEE.

Application of optimisation-based data mining techniques to tobacco control dataset

- Dzalilov, Zari, Zhang, J, Bagirov, Adil, Mammadov, Musa

  • Authors: Dzalilov, Zari , Zhang, J , Bagirov, Adil , Mammadov, Musa
  • Date: 2010
  • Type: Text , Journal article
  • Relation: International Journal of Lean Thinking Vol. 1, no. 1 (2010), p. 27-41
  • Full Text: false
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  • Description: Tobacco smoking is one of the leading causes of death around the world. Consequently, control of tobacco use is an important global public health issue. Tobacco control may be aided by development of theoretical and methodological frameworks for describing and understanding complex tobacco control systems. Linear regression and logistic regression are currently very popular statistical techniques for modeling and analyzing complex data in tobacco control systems. However, in tobacco markets, numerous interrelated factors nontrivially interact with tobacco control policies, such that policies and control outcomes are nonlinearly related.