John F Roddick

Flinders University · College of Science and Engineering

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understandin...

Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.

The task of finding correlations between items in a dataset-association mining-has received considerable attention, resulting in a variety of algorithms for both common and specialist mining tasks. After more than a decade of association mining research this paper presents a survey of research to date, with particular focus on the main algorithmic...

An instance of Hamiltonian cycle problem can be solved by converting it to an instance of Travelling salesman problem, assigning any choice of weights to edges of the underlying graph. In this note we demonstrate that, for difficult instances, choosing the edge weights to be the resistance distance between its two incident vertices is often a good...

Shuffled frog leaping algorithm is one of the popular used optimization algorithms. This algorithm includes the local search and global search two solving modes, but in this method only the worst frog from divided group is considered for improving location. In this paper, we propose a directional shuffled frog leaping algorithm (DSFLA) by introduci...

In the past few years, people have experienced advanced interest in the potential use of wireless sensor network in applications such as environment surveillance, military field protection and medical treatment. Usually hundreds even thousands of sensors are scattered randomly in remote environment. In generally, for the scalability of sensor netwo...

Optimization algorithm in swarm intelligence is getting more and more prevalent both in theoretical field and in real-world applications. Many nature-inspired algorithms in this domain have been proposed and employed in different applications. In this paper, a new QUATRE algorithm with sort strategy is proposed for global optimization. QUATRE algor...

In this paper, we concentrate on the modification of the evolved bat algorithm (EBA), designed for solving numerical optimization by utilizing the scheming idea of Artificial Bee Colony algorithm (ABC).Three roles of bat colony and six successive processes are realized to accelerate the convergence characteristic of the modificatory algorithm, name...

Wireless sensor networks (WSNs) are designed for a large scale monitoring applications such as military surveillance, medical treatment, environmental monitoring and industry management. In this network, usually hundreds or thousands of low-cost sensor nodes are deployed. These sensing nodes detect the events in the environment and pass an upstream...

An instance of Hamiltonian cycle problem can be solved by converting it to an instance of Travelling salesman problem, assigning any choice of weights to edges of the underlying graph. In this note we demonstrate that, for difficult instances, choosing the edge weights to be the resistance distance between its two incident vertices is often a good...

This paper presents an improvement of the flower pollination algorithm (FPA) for optimization localization issues in wireless sensor networks (WSN). A novel probabilistic is used to generate a new candidate of competition for simulation optimization operations. The actual population of tentative solutions does not employ, but a unique representativ...

Individual movement influences the spatial and social structuring of a population. Animals regularly use the same paths to move efficiently to familiar places, or to patrol and mark home ranges. We found that Australian sleepy lizards (Tiliqua rugosa), a monogamous species with stable pair-bonds, repeatedly used the same paths within their home ran...

Data 2.

Data 1.

In this paper, a novel algorithm for face recognition with one sample per person is proposed. The proposed algorithm is based on contourlet transformation. For simple prototype sample problem, many discriminant learning methods can not work. Because for most discriminant learning methods, the within class scatter of the prototype samples are very i...

Reversible watermarking (RW) based on position determination and three-pixel difference is proposed in this paper. The main idea of this paper is to obtain two difference values depending on a pixel pair. To achieve this purpose, for a pixel pair, its one pixel is predicted by the context of this pair to get its predicted value. By this way, we can...

Australia needs more qualified professionals in engineering, mathematics/science education, health and other sciences. The national focus on widening participation in higher education (HE) includes strengthening pathways from vocational education and training (VET). VET students often lack the mathematics skills necessary to articulate successfully...

Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns...

Recognizing optical character from document image of text mixed by figure has its wide applications such as document auto-reading. Segmenting the document region from text-mixed is a crucial step of this system. The segmentation procedure includes two stages, one is to extract the texture features of each block based on Gabor filter, and second is...

Various data mining techniques can be used to discover useful knowledge from large collections of data. However, there is a risk of disclosing sensitive information when data is shared between different organizations. The balance between legitimate mining needs and the protection of confidential knowledge when data is released or shared must be car...

With the increasing complexity of applications and user needs, recent research has shifted from a data-information level to a human semantic level interaction. Research has begun to address the increasing use and development of ontologies in various applications, strongly motivated by the semantic web initiative. However, existing conceptual models...

Australia needs more qualified professionals in the Science, Technology, Engineering, and Mathematics (STEM) areas. The national focus on widening participation in higher education (HE) includes strengthening pathways from vocational education and training (VET). VET students often lack the mathematics skills necessary to articulate successfully to...

The K-Nearest Neighbor algorithm is one of the commonly used methods for classification in machine learning and computational intelligence. A new research method and its improvement for the sleepy lizards based on the K-Nearest Neighbor algorithm and the traditional social network algorithms are proposed in this chapter. The famous paired living ha...

This paper presents a no-reference quality metric for evaluating the blocking artifacts in images. It is based on a finding that the blocking artifact has direct effect on the distribution of discrete Tchebichef moments. The image is first divided into target blocks that cover potential artifacts. Tchebichef moments are then extracted as the block...

Despite the flexibility offered by existing conceptual modelling techniques there remains a number of problems that are not well accommodated. Some of these relate to the relatively restricted (and static) modelling of relationships and a large number of these problems relate to the restricted notion of cardinality constraints. In this paper we revi...

Today, people can use various database techniques to discover useful knowledge from large collections of data. However, people also face the risk of disclosing sensitive information to competitor when the data is shared between different organizations. Thus, there is a balance between the legitimate mining need and protection of confidential knowle...

Kernel learning is becoming an important research topic in the area of machine learning, and it has wide applications in pattern recognition, computer vision, image and signal processing. Kernel learning provides a promising solution to nonlinear problems, including nonlinear feature extraction, classification and clustering. However, in kernel-bas...

Magnetic Resonance Imaging (MRI) data collection is influenced by SNR, hardware, image time, and other factors. The super-resolution analysis is a critical way to improve the imaging quality. This work presents a framework of super-resolution MRI via sparse reconstruction, and this method is promising to solve the data collection limitations. A nov...

Kernel based nonlinear feature extraction is feasible to extract the feature of image for classification.The current kernel-based method endures two problems: 1) kernel-based method is to use the data vector through transforming the image matrix into vector, which will cause the store and computing burden; 2) the parameter of kernel function has th...

With the development of the cloud computing, its security issues have got more and more attention. There is a great demand for the examining the content of data or packets in order to improve cloud security. In this paper, we propose a new algorithm about pattern matching for cloud security named Bit-Reduced automaton, First it performs inexact mat...

The relative dierence between two data values is of interest in a number of application domains including temporal and spatial applications, schema versioning, data warehousing (particularly data preparation), in- ternet searching, validation and error correction, and data mining. Moreover, consistency across systems in determining such distances a...

Copy-move is one of the most common image tampering method. Many schemes have been proposed to detect and locate the forged regions. However, many existing schemes fail when the copied region is rotated or ipped before being pasted. To solve the problem, this paper presents a new method for detecting the copy-move forgery. The image is first filter...

In this paper, a novel subspace learning algorithm, called neighborhood discriminant nearest feature line analysis (NDNFLA), is proposed. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) scatter and minimizing the within-class FL scatter. At the same time, the neighborhood is preserved in the...

In this paper, two novel image feature extraction algorithms based on directional filter banks and nearest feature line are proposed, which are named Single Directional Feature Line Discriminant Analysis (SD-NFDA) and Multiple Directional Feature Discriminant Line Analysis (MD-NFDA). SD-NFDA and MD-NFDA extract not only the statistic feature of sam...

A new research method for the sleepy lizards based on the KNN algorithm and the traditional social network algorithms is proposed in this paper. The famous paired habit of sleepy lizards is verified here based on our proposed algorithm. In addition, some common population characteristics of the lizards are also introduced by using the traditional s...

Constrained optimization problems compose a large part of real-world applications. More and more attentions have gradually been paid to solve this kind of problems. An improved particle swarm optimization (IPSO) algorithm based on feasibility rules is presented in this paper to solve constrained optimization problems. The average velocity of the sw...

Image recognition technologies have been used in many areas, and feature extraction of image is key step for image recognition. A novel feature extraction method using kernel self-optimized learning for image recognition. The scheme of image feature extraction includes textural extraction using Gabor wavelet, textural features reduction based on cl...

A novel subspace learning algorithm named neighborhood discriminant nearest feature line analysis (NDNFLA) is proposed in this paper. NDNFLA aims to find the discriminant feature of samples by maximizing the between-class feature line (FL) distances and minimizing the within-class FL distance. At the same time, theneighborhood is preserved in the f...

At present, data mining algorithms are largely the domain of governments, large organisations and academia where they provide useful insight into the data. However, without the ability to assure privacy protection, the availability of datasets for research purposes may be impaired. Moreover, privacy-preservation is essential if data mining is to be...

A number of trends in ICT have indicated that tools for the automated and semi-automated analysis of data will be essential for competitive and efficient organisations in the future. These trends include the rapidly increasing volumes of data being collected from an growing number of sources and the increasing complexity of that data. Both business...

Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search (SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on. Besides the applications to conventional optimization problems...

This paper proposed a Reduce Identical Event Transmission Algorithm (RIET). The algorithm can decide that which sensor nodes could send the event to sink node when sensor nodes sense a same even. Moreover, other nodes can save power because they didn’t send the same event. In our simulation, the RIET algorithm can enhance sensor nodes’ life time ab...

Some resolution strategies, such as SLD-resolution, are such that a derivation may be infinite even on a logic program that has a finite Herbrand universe. This paper introduces GOPT-resolution, a new deduction strategy for deriving solutions from a set of rules that improves on previous methods by preventing derivations that have infinite recursio...

In recent years, graph representations have been used extensively for modelling complicated structural information, such as circuits, images, molecular structures, biological networks, weblogs, XML documents and so on. As a result, frequent subgraph mining has become an important subfi�eld of graph mining. This paper presents a novel Frequent Patte...

Due to the inherent low-contrast in Electronic Portal Images (EPI), the perception quality of EPI has certain gap to the expectation of most physicians. It is essential to have effective post-processing methods to enhance the visual quality of EPI. However, only limited efforts had been paid to this issue in the past decade. To this problem, an int...

Multiple Description Coding (MDC) as an efficient method to solve the network fading problems, has been paid more and more attention these years. A novel three-channel MDC framework based on the orientation tree structure of the wavelet image and Vector Quantization (VQ) coding algorithm is introduced in this paper. The redundancy is introduced by...

Power consumption is one of the most important problems for wireless sensor networks because of the battery limitation in each sensor. This paper presents an ant colony optimization- (ACO-) based routing algorithm to reduce power consumption. First, a grade table is built and referred to generate several possible routing paths. Then, the ACO explor...

Hospitals are adept at capturing large volumes of highly multi-dimensional data about their activities including clinical, demographic, administrative, financial and, increasingly, outcome data (such as adverse events). Managing and understanding this data is difficult as hospitals typically do not have the staff and/or the expertise to assemble, q...

Temporal databases facilitate the support of historical information by providing functions for indicating the intervals during which a tuple was applicable (along one or more temporal dimensions). Because data are never deleted, only superceded, temporal databases are inherently append-only resulting, over time, in a large historical sequence of da...

Database and software systems are rarely stable following initial implementation. Although estimates differ, most agree that 50% or more of programmer effort arises as a result of system modifications after implementation [Lientz, B. P. 1983] and facilitating those changes is complicated if large numbers of programs or large quantities of data are...

Itemsets, which are treated as intermediate results in association mining, have attracted significant re- search due to the inherent complexity of their gen- eration. However, there is currently little literature focusing upon the interactions between itemsets, the nature of which may potentially contain valuable in- formation. This paper presents...

50-100 WORD SUMMARY Wittgenstein once stated that Ethics must be a condition of the world, like logic. The development of data mining has the capacity to compromise privacy in ways not previously possible, an issue not only exacerbated through inaccurate data and ethical abuse but also by a lagging legal framework which struggles, at times, to catc...

A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in a...

The value of knowledge obtainable by analysing large quantities of data is widely acknowledged. However, so-called primary or raw data may not always be available for knowledge discovery for several reasons. First, cooperating institutions that are interested in sharing knowledge may not be willing (or allowed) to disclose their primary data. Secon...

Mesodata modelling is a recently developed approach for enhancing a data model’s capabilities by providing for more advanced semantics to be associated with the domain of an attribute. Mesodata supplies both an inter-value structure to the domain and a set of operations applicable to that structure that may be used to facilitate additional function...

In this paper two categories of improvements are suggested that can be applied to most k-medoids-based algorithms - conceptual / algorithmic improvements, and implementational improvements. These include the revisiting of the accepted cases for swap comparison and the application of partial distance searching and previous medoid indexing to cluster...

Exploratory data mining is fundamental to foster- ing an appreciation of complex datasets. For large and continuously growing datasets, such as obtained by regular sampling of an organisation's commu- nications, the exploratory phase may never finish. This paper describes a methodology for exploratory data mining within an organisational communicat...

Given applications such as location based services and the spatio-temporal queries they may pose on a spatial network (e.g., road networks), the goal is to develop a simple and expressive model that honors the time dependence of the road network. The model must support the design of efficient algorithms for computing the frequent queries on the net...

To paraphrase Winograd (1992), we bring to our communities a tacit comprehension of right and wrong that makes social responsibility an intrinsic part of our culture. Our ethics are the moral principles we use to assert social responsibility and to perpetuate safe and just societies. Moreover, the introduction of new technologies can have a profoun...

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provi...

Database evolution can be considered a combination of schema evolution, in which the structure evolves with the addition and deletion of attributes and relations, together with domain evolution in which an attribute’s specification, semantics and/or range of allowable values changes. We present the results of an empirical investigation of the evolu...

The use of closed-set algorithms to generate condensed accurate representations of a dataset's frequent itemsets has been well documented. This paper presents a novel approach to incremental association mining in which the maintenance of the set of frequent itemsets is based upon the evolution of a closed-set lattice. This approach also creates a c...

Medical science has a long history characterised by incidents of extraordinary insights that have resulted in a paradigm shift in the methodologies and approaches used and have moved the discipline forward. While knowledge discovery has much to oer medicine, it cannot be done in ignorance of either this history or the norms of modern medical invest...

To date, most association rule mining algorithms have assumed that the domains of items are either discrete or, in a limited number of cases, hierarchical, categorical or linear. This constrains the search for interesting rules to those that satisfy the specified quality metrics as independent values or as higher level concepts of those values. How...

There are a number of issues for information systems which are required to collect data urgently that are not well accommodated by current conceptual mod- elling methodologies and as a result the modelling step (and the use of databases) is often omitted. Such issues include the fact that • the number of instances for each entity are rel- atively l...

The detection of unusual or anomalous data is an im- portant function in automated data analysis or data mining. However, the diversity of anomaly detection algorithms shows that it is often dicult to deter- mine which algorithms might detect anomalies given any random dataset. In this paper we provide a par- tial solution to this problem by elevat...

The quality of data mining results is largely dependent on the ability to accommodate context and user requirements within the mining process. This is done effectively within the pre-processing and presentation stages, however the analysis (or mining) stage remains relatively autonomous and opaque with user input commonly limited to parameter setti...

Outlier (or anomaly) detection is an important problem for many domains, including fraud detec- tion, risk analysis, network intrusion and medical diagnosis, and the discovery of significant outliers is becoming an integral aspect of data mining. This paper presents CURIO, a novel algorithm that uses quantisation and implied distance metrics to pro...