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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
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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.

Novel data mining techniques for incompleted clinical data in diabetes management

- Jelinek, Herbert, Yatsko, Andrew, Stranieri, Andrew, Venkatraman, Sitalakshmi

  • Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi
  • Date: 2014
  • Type: Text , Journal article
  • Relation: British Journal of Applied Science & Technology Vol. 4, no. 33 (2014), p. 4591-4606
  • Relation: https://doi.org/10.9734/BJAST/2014/11744
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  • Description: An important part of health care involves upkeep and interpretation of medical databases containing patient records for clinical decision making, diagnosis and follow-up treatment. Missing clinical entries make it difficult to apply data mining algorithms for clinical decision support. This study demonstrates that higher predictive accuracy is possible using conventional data mining algorithms if missing values are dealt with appropriately. We propose a novel algorithm using a convolution of sub-problems to stage a super problem, where classes are defined by Cartesian Product of class values of the underlying problems, and Incomplete Information Dismissal and Data Completion techniques are applied for reducing features and imputing missing values. Predictive accuracies using Decision Branch, Nearest Neighborhood and Naïve Bayesian classifiers were compared to predict diabetes, cardiovascular disease and hypertension. Data is derived from Diabetes Screening Complications Research Initiative (DiScRi) conducted at a regional Australian university involving more than 2400 patient records with more than one hundred clinical risk factors (attributes). The results show substantial improvements in the accuracy achieved with each classifier for an effective diagnosis of diabetes, cardiovascular disease and hypertension as compared to those achieved without substituting missing values. The gain in improvement is 7% for diabetes, 21% for cardiovascular disease and 24% for hypertension, and our integrated novel approach has resulted in more than 90% accuracy for the diagnosis of any of the three conditions. This work advances data mining research towards achieving an integrated and holistic management of diabetes. - See more at: http://www.sciencedomain.org/abstract.php?iid=670&id=5&aid=6128#.VCSxDfmSx8E

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

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

Application of optimisation-based data mining techniques to medical data sets: A comparative analysis

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

  • Authors: Dzalilov, Zari , Bagirov, Adil , Mammadov, Musa
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: IMMM 2102: The Second International Conference on Advances in Information Mining and Management p. 41-46
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  • Description: Abstract - Computational methods have become an important tool in the analysis of medical data sets. In this paper, we apply three optimisation-based data mining methods to the following data sets: (i) a cystic fibrosis data set and (ii) a tobacco control data set. Three algorithms used in the analysis of these data sets include: the modified linear least square fit, an optimization based heuristic algorithm for feature selection and an optimization based clustering algorithm. All these methods explore the relationship between features and classes, with the aim of determining contribution of specific features to the class outcome. However, the three algorithms are based on completely different approaches. We apply these methods to solve feature selection and classification problems. We also present comparative analysis of the algorithms using computational results. Results obtained confirm that these algorithms may be effectively applied to the analysis of other (bio)medical data sets

Zero-day malware detection based on supervised learning algorithms of API call signatures

- Alazab, Mamoun, Venkatraman, Sitalakshmi, Watters, Paul, Alazab, Moutaz

  • Authors: Alazab, Mamoun , Venkatraman, Sitalakshmi , Watters, Paul , Alazab, Moutaz
  • Date: 2011
  • Type: Text , Conference proceedings
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  • Description: Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k-Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO - PolyKernel, SMO - Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today. © 2011, Australian Computer Society, Inc.
  • Description: 2003009506

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.

A Grobner-Shirshov Algorithm for Applications in Internet Security

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

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

Experimental investigation of clasification algorithms for ITS dataset

- Yearwood, John, Kang, Byeongho, Kelarev, Andrei

  • Authors: Yearwood, John , Kang, Byeongho , Kelarev, Andrei
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: PKAW-08, Pacific Rim Knowledge Acquisition Workshop 2008, as part of PRICAI 2008, Tenth Pacific Rim p. 262-272
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  • Description: This article is devoted to experimental investigation of classification algorithms for analysis of ITS dataset. We introduce and consider a novel k-committees alogorithm for classification and compare it with the discrete k- means and nearest neighbour algorithms. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel algorithms and adjust familiar ones. We present the results of experiments comparing the efficiency of three classification methods in their ability to achieve agreement with classes published in the biological literature before. It turns out that our algorithms are efficient and can be used to obtain biologically significant classifications. A simplified version of a synthetic dataset, where the k-committees classifier out performs k-means and Nearest Neighbour classifiers, is also presented.
  • Description: E1

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.

Mining associated sensor patterns for data stream of wireless sensor networks

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2013
  • Type: Text , Conference proceedings
  • Relation: 8th ACM International Workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, PM2HW2N 2013, Barcelona; Spain; 3rd-8th November 2013 p. 91-98
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  • Description: WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.

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

Structure learning of Bayesian networks using a new unrestricted dependency algorithm

- Taheri, Sona, Mammadov, Musa

  • Authors: Taheri, Sona , Mammadov, Musa
  • Date: 2012
  • Type: Text , Conference proceedings
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  • Description: Bayesian Networks have deserved extensive attentions in data mining due to their efficiencies, and reasonable predictive accuracy. A Bayesian Network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency between two variables. Constructing a Bayesian Network from data is the learning process that is divided in two steps: learning structure and learning parameter. In many domains, the structure is not known a priori and must be inferred from data. This paper presents an iterative unrestricted dependency algorithm for learning structure of Bayesian Networks for binary classification problems. Numerical experiments are conducted on several real world data sets, where continuous features are discretized by applying two different methods. The performance of the proposed algorithm is compared with the Naive Bayes, the Tree Augmented Naive Bayes, and the k

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

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

Share-frequent sensor patterns mining from wireless sensor network data

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Parallel and Distributed Systems Vol. 26, no. 12 (2015), p. 3471-3484
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  • Description: Mining interesting knowledge from the huge amount of data gathered from WSNs is a challenge. Works reported in literature use support metric-based sensor association rules which employ the occurrence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern is not a sufficient indicator for finding meaningful patterns because it only reflects the number of epochs which contain that pattern in the dataset. The share measure of sensorsets could discover useful knowledge about trigger values associated with a sensor. Here, we propose a new type of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs can find a correlation among a set of sensors and hence can improve the performance of WSNs in a resource management process. In this paper, a share-frequent sensor pattern tree (ShrFSP-Tree) has been proposed to facilitate a pattern growth mining technique to discover SFSPs from WSN data. We also present a parallel and distributed method where the ShrFSP-Tree is enhanced into PShrFSP-Tree and its performance is investigated for both homogeneous and heterogeneous systems. Results show that our method is time and memory efficient in finding SFSPs than the existing most efficient algorithms.

An efficient classification using support vector machines

- Ruan, Ning, Chen, Yi, Gao, David

  • Authors: Ruan, Ning , Chen, Yi , Gao, David
  • Date: 2013
  • Type: Text , Conference paper
  • Relation: Proceedings of 2013 Science and Information Conference, SAI 2013 p. 585-589
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  • Description: Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization problems are equivalent to a unified concave maximization problem over a convex set and hence can be solved efficiently by existing optimization techniques. © 2013 The Science and Information Organization.

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.

Mining associated patterns from wireless sensor networks

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

  • Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
  • Date: 2015
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Computers Vol. 64, no. 7 (2015), p. 1998-2011
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  • Description: Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

Functional specialisation and socio-economic factors in population change : A clustering study in non-metropolitan Australia

- Mardaneh, Karim

  • Authors: Mardaneh, Karim
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Urban Studies Vol. 53, no. 8 (2015), p. 1591-1616
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  • Description: Although research has examined population growth and decline using functional specialisation, little attention has been paid to the possible combined effects of functional specialisation and socio-economic factors on population change. Using the Australian Bureau of Statistics Census Data 2001–2006 for statistical local areas, this study presents an investigation of the role of both functional specialisation and socio-economic factors in population change in non-metropolitan areas under the sustenance framework. The uniqueness of the study is twofold. Conceptually it develops a framework to compare the combined role of functional specialisation and socio-economic factors on population change; and, empirically it uses data mining (cluster analysis) techniques to investigate the extent of this combined role. The results show the significance of both functional specialisation and socio-economic factors. Policy implications of the study indicate the need to examine regional development and population change in relation to functional specialisation and socio-economic factors and their impact on viability of non-metropolitan areas. © Urban Studies Journal Limited 2015.

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.