Social Impact of Mining Survey : Aggregate Results Queensland Communities

This is the final report from a study into the social impact of mining in Queensland.

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

Mining positive and negative patterns for relevance feature discovery

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Rough sets based reasoning and pattern mining for a two-stage information filtering system

This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern- based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experiments have been conducted to compare the proposed two-stage filtering (T-SM) model with other possible "term-based + pattern-based" or "term-based + term-based" IF models. The results based on the RCV1 corpus show that the T-SM model significantly outperforms other types of "two-stage" IF models.

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.

Thermal stability of the ‘cave’ mineral brushite CaHPO4•2H2O -mechanism of formation and decomposition

Thermogravimetry combined with evolved gas mass spectrometry has been used to ascertain the stability of the ‘cave’ mineral brushite. X-ray diffraction shows that brushite from the Jenolan Caves is very pure. Thermogravimetric analysis coupled with ion current mass spectrometry shows a mass loss at 111°C due to loss of water of hydration. A further decomposition step occurs at 190°C with the conversion of hydrogen phosphate to a mixture of calcium ortho-phosphate and calcium pyrophosphate. TG-DTG shows the mineral is not stable above 111°C. A mechanism for the formation of brushite on calcite surfaces is proposed, and this mechanism has relevance to the formation of brushite in urinary tracts.

Raman spectroscopy of newberyite Mg(PO3OH)•3H2O – a cave mineral

Newberyite Mg(PO3OH)•3H2O is a mineral found in caves such as from Moorba cave, Jurien Bay, Western Australia, the Skipton Lava tubes (SW of Ballarat, Victoria, Australia) and in the Petrogale Cave (Madura , Eucla, Western Australia). Because these minerals contain oxyanions, hydroxyl units and water, the minerals lend themselves to spectroscopic analysis. Raman spectroscopy can investigate the complex paragenetic relationships existing between a number of ‘cave’ minerals. The intense sharp band at 982 cm-1 is assigned to the PO43- ν1 symmetric stretching mode. Low intensity Raman bands at 1152, 1263 and 1277 cm-1 are assigned to the PO43- ν3 antisymmetric stretching vibrations. Raman bands at 497 and 552 cm-1 are attributed to the PO43- ν4 bending modes. An intense Raman band for newberyite at 398 cm-1 with a shoulder band at 413 cm-1 is assigned to the PO43- ν2 bending modes. The values for the OH stretching vibrations provide hydrogen bond distances of 2.728Å (3267 cm-1), 2.781Å (3374cm-1), 2.868Å (3479 cm-1), and 2.918Å (3515 cm-1). Such hydrogen bond distances are typical of secondary minerals. Estimates of the hydrogen-bond distances have been made from the position of the OH stretching vibrations and show a wide range in both strong and weak bonds.

Raman spectroscopy of stercorite H(NH4)Na(PO4)•4H2O – a cave mineral from Petrogale Cave, Madura, Eucla, Western Australia

Raman spectroscopy complimented with infrared spectroscopy has been used to characterise the mineral stercorite H(NH4)Na(PO4)·4H2O. The mineral stercorite originated from the Petrogale Cave, Madura, Eucla, Western Australia. This cave is one of many caves in the Nullarbor Plain in the South of Western Australia. These caves have been in existence for eons of time and have been dated at more than 550 million years old. The mineral is formed by the reaction of bat guano chemicals on calcite substrates. A single Raman band at 920 cm−1 defines the presence of phosphate in the mineral. Antisymmetric stretching bands are observed in the infrared spectrum at 1052, 1097, 1135 and 1173 cm−1. Raman spectroscopy shows the mineral is based upon the phosphate anion and not the hydrogen phosphate anion. Raman and infrared bands are found and assigned to PO43−, H2O, OH and NH stretching vibrations. The detection of stercorite by Raman spectroscopy shows that the mineral can be readily determined; as such the application of a portable Raman spectrometer in a ‘cave’ situation enables the detection of minerals, some of which may remain to be identified.

Are the ‘cave’ minerals archerite (K,NH4)H2PO4 and biphosphammite (K,NH4)H2PO4 identical? A molecular structural study

The molecular structure of the mineral archerite ((K,NH4)H2PO4) has been determined and compared with that of biphosphammite ((NH4,K)H2PO4). Raman spectroscopy and infrared spectroscopy has been used to characterise these ‘cave’ minerals. Both minerals originated from the Murra-el-elevyn Cave, Eucla, Western Australia. The mineral is formed by the reaction of the chemicals in bat guano with calcite substrates. Raman and infrared bands are assigned to H2PO4-, OH and NH stretching vibrations. The Raman band at 981 cm-1 is assigned to the HOP stretching vibration. Bands in the 1200 to 1800 cm-1 region are associated with NH4+ bending modes. The molecular structure of the two minerals appear to be very similar, and it is therefore concluded that the two minerals are identical.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.