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Environmental damage has been of much concern, particularly in coastal areas and the oceans, given climate change and the drastic effects of pollution and extreme climate events. Our present-day analytical capabilities, along with advancements in information acquisition techniques such as remote sensing, can be utilised for the management and study of coral reef ecosystems. In this paper, we present Reef-Insight, an unsupervised machine learning framework that features advanced clustering methods and remote sensing for reef habitat mapping. Our framework compares different clustering methods for reef habitat mapping using remote sensing data. We evaluate four major clustering approaches based on qualitative and visual assessments which include k-means, hierarchical clustering, Gaussian mixture model, and density-based clustering. We utilise remote sensing data featuring the One Tree Island reef in Australia's Southern Great Barrier Reef. Our results indicate that clustering methods using remote sensing data can well identify benthic and geomorphic clusters in reefs when compared with other studies. Our results indicate that Reef-Insight can generate detailed reef habitat maps outlining distinct reef habitats and has the potential to enable further insights for reef restoration projects. [ABSTRACT FROM AUTHOR]

Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [ABSTRACT FROM AUTHOR]

The national health insurance system in Australia records details on medical services and claims provided to its population. An effective method to the discovery of temporal behavioral patterns in the data set is proposed in this paper. The method consists of a two-step approach which is applied recursively to the data set. First, a clustering algorithm is used to segment the data into classes. Then, hidden Markov models are employed to find the underlying temporal behavioral patterns. These steps are applied recursively to features extracted from the data set until convergence. The main objective is to minimize the misclassification of patient profiles into various classes. This results in a hierarchical tree model consisting of a number of classes; each class groups similar patient temporal behavioral patterns together. The capabilities of the proposed method are demonstrated through the application to a subset of the Australian national health insurance data set. It is shown that the proposed method not only clusters data into various categories of interest, but it also automatically marks the periods in which similar temporal behavioral patterns occurred. [ABSTRACT FROM AUTHOR]

Questions: Most clustering methods assume data are structured as discrete hyperspheroidal clusters to be evaluated by measures of central tendency. If vegetation data do not conform to this model, then vegetation data may be clustered incorrectly. What are the implications for cluster stability and evaluation if clusters are of irregular shape or density? Location: Southeast Australia. Methods: We define misplacement as the placement of a sample in a cluster other than (distinct from) its nearest neighbor and hypothesize that optimizing homogeneity incurs the cost of higher rates of misplacement. Chameleon is a graph‐theoretic algorithm that emphasizes interconnectivity and thus is sensitive to the shape and distribution of clusters. We contrasted its solutions with those of traditional nonhierarchical and hierarchical (agglomerative and divisive) approaches. Results: Chameleon‐derived solutions had lower rates of misplacement and only marginally higher heterogeneity than those of k‐means in the range of 15–60 clusters, but their metrics converged with larger numbers of clusters. Solutions derived by agglomerative clustering had the best metrics (and divisive clustering the worst) but both produced inferior high‐level solutions to those of Chameleon by merging distantly‐related clusters. Conclusions: Graph‐theoretic algorithms, such as Chameleon, have an advantage over traditional algorithms when data exhibit discontinuities and variable structure, typically producing more stable solutions (due to lower rates of misplacement) but scoring lower on traditional metrics of central tendency. Advantages are less obvious in the partitioning of data from continuous gradients; however, graph‐based partitioning protocols facilitate the hierarchical integration of solutions. [ABSTRACT FROM AUTHOR]

Petrol directly impacts the ability of and extent to which households can engage in day-to-day activities and ultimately directly influences the aggregate economic activity. Petrol price increases can lead to major economic disruption, especially among the most vulnerable, such as low-income families. In Australia, petrol prices differ substantially between metropolitan and regional areas, and regional drivers must pay more for purchasing petrol than those in capital cities. This research explored why retail petrol prices vary from one city to another within and between Australian regions. In this study, clustering methods and panel models will be used to identify factors that cause price differences. The findings revealed that a considerable part of the price differences arose from specific characteristics of cities that influence the demand and supply of petrol, thereby causing variations in price decisions. Petrol prices were substantially influenced by wholesale petrol prices, oil prices, petrol and diesel vehicles, population density, station density, and public transport accessibility. These factors are the main determinants that contribute significantly to price variations between Australian cities. The findings provide critical information for economic agents that interact in this market. From a social welfare perspective, government authorities can consider these factors to improve living standards and consumers' welfare under price pressure in regional cities. [ABSTRACT FROM AUTHOR]

Abstract: The Australian River Assessment System (AusRivAS) is a nation-wide programme designed to assess the health of Australian rivers and streams. In order to produce river health assessments, the AusRivAS method uses the outcomes of cluster analysis applied to macroinvertebrate data from a number of different locations. At present, the clustering step is conducted using the statistical Unweighted Pair Group Arithmetic Averaging (UPGMA) method. A potential shortcoming of this approach is that it uses a linear performance measure for grouping similar data points. A recently developed approach for clustering ecological data (MIR-max) overcomes this limitation by using mutual information as the performance measure. However, MIR-max uses a hill-climbing approach for optimising mutual information, which could become trapped in local optima of the search space. In this paper, the potential of using evolutionary algorithms (EAs), such as genetic algorithms and ant colony optimisation algorithms, for maximising the mutual information of ecological data clusters is investigated. The MIR-max and EA-based approaches are applied to the South Australian combined season riffle AusRivAS data, and the results obtained are compared with those obtained using the UPGMA method. The results indicate that the overall mutual information values of the clusters obtained using MIR-max and the EA-based approaches are significantly higher than those obtained using the UPGMA method, and that the use of genetic and ant colony optimisation algorithms is successful in determining clusters with higher overall mutual information values compared with those obtained using MIR-max for the case study considered. [Copyright &y& Elsevier]