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A Grobner-Shirshov Algorithm for Applications in Internet Security

- Kelarev, Andrei, Yearwood, John, Watters, Paul, Wu, Xinwen, Ma, Liping, Abawajy, Jemal, Pan, L.

Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football

- Jelinek, Herbert, Kelarev, Andrei, Robinson, Dean, Stranieri, Andrew, Cornforth, David

  • Authors: Jelinek, Herbert , Kelarev, Andrei , Robinson, Dean , Stranieri, Andrew , Cornforth, David
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Applied Soft Computing Vol. 14, no. PART A (2014), p. 81-87
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  • Description: This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05). © 2013 Published by Elsevier B.V. All rights reserved.
  • Description: C1

Video games' educational evaluation model based on BP neural network

- Wang, Wei, Maruatona, Omaru, Qian, Huang

  • Authors: Wang, Wei , Maruatona, Omaru , Qian, Huang
  • Date: 2011
  • Type: Text , Conference proceedings
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  • Description: Evaluating the education value of video games is a complex task. Based on the educational evaluation system for video games, we establish a corresponding automated evaluation model using the Back-propagation (BP) neural network technology. After training with the expert knowledge, the model not only has the experience of experts, but also has the computational capacity to evaluate new cases. © 2011 IEEE.

Rees matrix constructions for clustering of data

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

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.

Automatic sleep stage identification: difficulties and possible solutions

- Sukhorukova, Nadezda, Stranieri, Andrew, Ofoghi, Bahadorreza, Vamplew, Peter, Saleem, Muhammad Saad, Ma, Liping, Ugon, Adrien, Ugon, Julien, Muecke, Nial, Amiel, Hélène, Philippe, Carole, Bani-Mustafa, Ahmed, Huda, Shamsul, Bertoli, Marcello, Levy, P, Ganascia, J.G

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.

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

Isolation forest

- Liu, Fei, Ting, Kaiming, Zhou, Zhi-Hua

  • Authors: Liu, Fei , Ting, Kaiming , Zhou, Zhi-Hua
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Proceedings of the Eighth IEEE International Conference on Data Mining p. 413-422
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  • Description: Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our best knowledge, the concept of isolation has not been explored in current literature. The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement. Our empirical evaluation shows that iForest performs favourably to ORCA, a near-linear time complexity distance-based method, LOF and random forests in terms of AUC and processing time, and especially in large data sets. iForest also works well in high dimensional problems which have a large number of irrelevant attributes, and in situations where training set does not contain any anomalies.

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

New diagonal bundle method for clustering problems in large data sets

- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona

  • Authors: Karmitsa, Napsu , Bagirov, Adil , Taheri, Sona
  • Date: 2017
  • Type: Text , Journal article
  • Relation: European Journal of Operational Research Vol. 263, no. 2 (2017), p. 367-379
  • Relation: http://purl.org/au-research/grants/arc/DP140103213
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  • Description: Clustering is one of the most important tasks in data mining. Recent developments in computer hardware allow us to store in random access memory (RAM) and repeatedly read data sets with hundreds of thousands and even millions of data points. This makes it possible to use conventional clustering algorithms in such data sets. However, these algorithms may need prohibitively large computational time and fail to produce accurate solutions. Therefore, it is important to develop clustering algorithms which are accurate and can provide real time clustering in large data sets. This paper introduces one of them. Using nonsmooth optimization formulation of the clustering problem the objective function is represented as a difference of two convex (DC) functions. Then a new diagonal bundle algorithm that explicitly uses this structure is designed and combined with an incremental approach to solve this problem. The method is evaluated using real world data sets with both large number of attributes and large number of data points. The proposed method is compared with two other clustering algorithms using numerical results. © 2017 Elsevier B.V.

A semantic method to information extraction for decision support systems

- Ofoghi, Bahadorreza, Yearwood, John, Ghosh, Ranadhir

  • Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
  • Date: 2006
  • Type: Text , Conference proceedings
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  • Description: In this paper, we describe a novel schema for a more semantic text mining process which results in more comprehensive decision making activity by decision support systems via providing more effective and accurate textual information. The utility of two semantic lexical resources; Frame Net and Word Net, in extracting required text snippets from unstructured free texts yields a better and more accurate information extraction process to deliver more precise information either to a DSS or to a decision maker. We explain how the usage of these lexical resources could elevate a focused text mining process which could be applied to an information provider system in a decision support paradigm. The preliminary results obtained after a starter experiment show that the hybrid information extraction schema performs well on some semantic failure situations.
  • Description: 2003010644

Classification systems based on combinatorial semigroups

- Abawajy, Jemal, Kelarev, Andrei

  • Authors: Abawajy, Jemal , Kelarev, Andrei
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Semigroup forum Vol. 86, no. 3 (2013), p. 603-612
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  • Description: The present article continues the investigation of constructions essential for applications of combinatorial semigroups to the design of multiple classification systems in data mining. Our main theorem gives a complete description of all optimal classification systems defined by one-sided ideals in a construction based on combinatorial Rees matrix semigroups. It strengthens and generalizes previous results, which handled the more narrow case of two-sided ideals.

Isolation-based anomaly detection

- Liu, Fei, Ting, Kaiming, Zhou, Zhi-Hua

  • Authors: Liu, Fei , Ting, Kaiming , Zhou, Zhi-Hua
  • Date: 2012
  • Type: Text , Journal article
  • Relation: ACM Transactions on Knowledge Discovery from Data Vol. 6, no. 1 (2012), p. 1-39
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  • Description: Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods. As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample

Data mining Traditional Chinese Medicine (TCM) : Lessons learnt from mining in law and allopathic medicine

- Stranieri, Andrew, Sahama, Tony

  • Authors: Stranieri, Andrew , Sahama, Tony
  • Date: 2012
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theoretical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine. © 2012 IEEE.
  • Description: 2003009797

An efficient data extraction framework for mining wireless sensor networks

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: 23rd International Conference, ICONIP 2016; Kyoto, Japan; 16th-21st October 2016; published in Neural Information Processing, Part III (Lecture Notes in Computer Science series) Vol. 9949, p. 491-498
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  • Description: Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor's activities over time from the sensor database that attached with a well-equipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associated-correlated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

Fake file detection in P2P networks by consensus and reputation

- Watters, Paul, Layton, Robert

  • Authors: Watters, Paul , Layton, Robert
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Previous research [1] has indicated that reputation scores can be used as the basis for trust computation in P2P networks. In this paper, we use reputation scores calculated from P2P search engine rating sites to determine whether a torrent is likely to be linked to a fake file (or not). Our results indicate clear separability between files which are fake and which are genuine, assuming the integrity of the "community" ratings provided by specific subcultural groups [2]. Suggestions for more sophisticated reputation-based scoring are also provided. © 2011 Crown.

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.

Rule-based classifiers and meta classifiers for identification of cardiac autonomic neuropathy progression

- Jelinek, Herbert, Kelarev, Andrei, Stranieri, Andrew, Yearwood, John

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