A data mining application of the incidence semirings

This paper is devoted to a combinatorial problem for incidence semirings, which can be viewed as sets of polynomials over graphs, where the edges are the unknowns and the coefficients are taken from a semiring. The construction of incidence rings is very well known and has many useful applications. The present article is devoted to a novel application of the more general incidence semirings. Recent research on data mining has motivated the investigation of the sets of centroids that have largest weights in semiring constructions. These sets are valuable for the design of centroid-based classification systems, or classifiers, as well as for the design of multiple classifiers combining several individual classifiers. Our article gives a complete description of all sets of centroids with the largest weight in incidence semirings.

Reluctant relaxation: The end of the iron ore export embargo and the origins of Australia's mining boom, 1960-1966

The Australian government embargoed any export of iron ore between 1938 and 1960. Joseph Lyons’s government imposed the ban on the eve of World War II for a strategic reason: to prevent the Japanese from importing ore from Yampi Sound in Western Australia. Another consideration, which underpinned the retention of the ban for more than two decades, was the Commonwealth of Australia's perception that Australia's iron ore reserves were limited. In the space of a few years after the partial lifting of the embargo in 1960, world-class reserves of iron ore, mainly in Western Australia, were discovered. Mined and exported from the mid-1960s, iron ore would become, in time, Australia’s best export earner. This article explores the reasons behind the lifting of the ban and how the relaxation of the embargo in stages between 1960 and 1966 shaped the emerging iron ore industry and therefore Australia’s mining boom.

A study of children’s musical preference: A data mining approach

Musical preference has long been a research interest in the field of music education, and studies consistently confirm the importance of musical preference in one’s musical learning experiences. However, only a limited number of studies have been focussed on the field of early childhood education (e.g., Hargreaves, North, & Tarrant, 2006; Roulston, 2006). Further, among these limited early childhood studies, few of them discuss children’s musical preference in both the East and the West. There is very limited literature (e.g., Faulkner et al., 2010; Szymanska, 2012) which explores the data by using a data mining approach. This study aims to bridge the research gaps by examining children’s musical preference in Hong Kong and in South Australia by applying a data mining technique – Self Organising Maps (SOM), which is a clustering method that groups similar data objects together. The application of SOM is new in the field of early childhood education and also in the study of children’s musical preference. This paper specifically aims to expand a previous study (Yim & Ebbeck, 2009) by conducting deeper investigations into the existing datasets, for the purpose of uncovering insights that have not been identified through data mining approach.

Quantitatively evaluating the effects of price promotions using data mining

Price promotions (also called discount promotions), i.e. short-term temporary price reductions for selected items (Hermann 1989), are frequently used in sales promotions. The main objective of price promotions is to boost sales and increase profits. Quantitative evaluation of the effects of price promotions (QEEPP) is essential and important for sales managers to analyse historical price promotions and informative for devising more effective promotional strategies in the future. However, most previous studies only provide insights into the effects of discount promotions from some specific prospectives, and no approaches have been proposed for comprehensive evaluation of the effects of discount promotions. For example, Hinkle [1965] discovered that price promotions in the off-season are more favourable, and the effects of price promotions are stronger for new products. Peckham [1973] found that price promotions have no impact on long-term trend. Blattberg et al. [1978] identified that different segments respond to price promotions in different ways. Rockney [1991] discovered three basic types of effects: effects on discounted items, effects on substitutes and effects on complementary items.

Auditor industry specialisation, service bundling and partner effects in a mining-dominated city

This study examines auditor industry specialisation effects in Perth, a remote mining town in Australia characterised by a large number of small, homogeneous firms. We consider the impact of leadership by the non-Big 4 auditor BDO Kendalls (BDO) for a sample of 371 mining development stage entities (MDSE's). After controlling for factors known to determine audit fees, we find no evidence of auditor industry leadership fee premiums accruing to (BDO), a result robust to a range of sensitivity tests including the broadening of tests Australia-wide. However, when the dependent variable is redefined to the total 'bundle' of services provided by the audit firm (including audit and non-audit fees), the industry leader is shown to earn a fee premium suggesting BDO uses audits as a conduit to supply higher margin non-audit services. Our findings suggest that strategic pricing by industry leaders may not be confined to Big 4 firms.

A data mining framework for electricity consumption analysis from meter data

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

A data mining approach for detection of self-propagating worms

In this paper we demonstrate our signature based detector for self-propagating worms. We use a set of worm and benign traffic traces of several endpoints to build benign and worm profiles. These profiles were arranged into separate n-ary trees. We also demonstrate our anomaly detector that was used to deal with tied matches between worm and benign trees. We analyzed the performance of each detector and also with their integration. Results show that our signature based detector can detect very high true positive. Meanwhile, the anomaly detector did not achieve high true positive. Both detectors, when used independently, suffer high false positive. However, when both detectors were integrated they maintained a high detection rate of true positive and minimized the false positive

What do indigenous communities think of the CSR practices of mining companies?

This paper examines how one indigenous community in the Western Province of Papua New Guinea (PNG) views the social responsibility initiatives of OK Tedi Mining Ltd (OTML). This mining operation has been controversial since its inception, and various operators of the mine have sought to engage the community and to undertake a number of CSR-related projects. Insights gained from four focus groups amongst the Ok Tedi River indigenous communities show that while some members of the community are satisfied with the company’s efforts at the macro level, many have reservations about the effectiveness of the programs at the micro level on the village and family unit. The implementation of CSR activities are slow and in many instances do not effectively address stakeholder concerns.

Efficient mining of top-k breaker emerging subgraph patterns from graph datasets

This paper introduces a new type of discriminative subgraph pattern called breaker emerging subgraph pattern by introducing three constraints and two new concepts: base and breaker. A breaker emerging sub-graph pattern consists of three subpatterns: a con-strained emerging subgraph pattern, a set of bases and a set of breakers. An efficient approach is pro-posed for the discovery of top-k breaker emerging sub-graph patterns from graph datasets. Experimental re-sults show that the approach is capable of efficiently discovering top-k breaker emerging subgraph patterns from given datasets, is more efficient than two previ-ous methods for mining discriminative subgraph pat-terns. The discovered top-k breaker emerging sub-graph patterns are more informative, more discrim-inative, more accurate and more compact than the minimal distinguishing subgraph patterns. The top-k breaker emerging patterns are more useful for sub-structure analysis, such as molecular fragment analy-sis. © 2009, Australian Computer Society, Inc.

Mining streams of short text for analysis of world-wide event evolutions

Streams of short text, such as news titles, enable us to effectively and efficiently learn the real world events that occur anywhere and anytime. Short text messages that are companied by timestamps and generally brief events using only a few words differ from other longer text documents, such as web pages, news stories, blogs, technical papers and books. For example, few words repeat in the same news titles, thus frequency of the term (i.e., TF) is not as important in short text corpus as in longer text corpus. Therefore, analysis of short text faces new challenges. Also, detecting and tracking events through short text analysis need to reliably identify events from constant topic clusters; however, existing methods, such as Latent Dirichlet Allocation (LDA), generates different topic results for a corpus at different executions. In this paper, we provide a Finding Topic Clusters using Co-occurring Terms (FTCCT) algorithm to automatically generate topics from a short text corpus, and develop an Event Evolution Mining (EEM) algorithm to discover hot events and their evolutions (i.e., the popularity degrees of events changing over time). In FTCCT, a term (i.e., a single word or a multiple-words phrase) belongs to only one topic in a corpus. Experiments on news titles of 157 countries within 4 months (from July to October, 2013) demonstrate that our FTCCT-based method (combining FTCCT and EEM) achieves far higher quality of the event's content and description words than LDA-based method (combining LDA and EEM) for analysis of streams of short text. Our method also visualizes the evolutions of the hot events. The discovered world-wide event evolutions have explored some interesting correlations of the world-wide events; for example, successive extreme weather phenomenon occur in different locations - typhoon in Hong Kong and Philippines followed hurricane and storm flood in Mexico in September 2013. © 2014 Springer Science+Business Media New York.

Mining permission patterns for contrasting clean and malicious android applications

An Android application uses a permission system to regulate the access to system resources and users' privacy-relevant information. Existing works have demonstrated several techniques to study the required permissions declared by the developers, but little attention has been paid towards used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. A benchmark malware dataset and a dataset of 1227 clean applications has been collected by us to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns. © 2013 Elsevier B.V. All rights reserved.