IPMA : Indirect patterns mining algorithm

Indirect pattern is considered as valuable and hidden information in transactional database. It represents the property of high dependencies between two items that are rarely occurred together but indirectly appeared via another items. Indirect pattern mining is very important because it can reveal a new knowledge in certain domain applications. Therefore, we propose an Indirect Pattern Mining Algorithm (IPMA) in an attempt to mine the indirect patterns from data repository. IPMA embeds with a measure called Critical Relative Support (CRS) measure rather than the common interesting measures. The result shows that IPMA is successful in generating the indirect patterns with the various threshold values.

Policy change and industry associability: the Australian mining sector

In this paper we use evidence from the recent trajectories of mining industry associations in the Australian federation to argue for the significance of institutional explanations for the formation and maintenance of interest groups. We argue that the recent lack of consultation by the Commonwealth government with the Minerals Council of Australia over resources rent taxation proposals reflected a weakness that resulted from the shifting basis of associability stemming from institutional changes.

Incremental learning of temporally-coherent gaussian mixture models

In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive oneby- one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed, which is increased (or reduced) when enough evidence for a new component is seen. This is deduced from the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions

Incremental learning of temporally-coherent Gaussian mixture models

In this paper we address the problem of learning Gaussian Mixture Models (GMMs) incrementally. Unlike previous approaches which universally assume that new data comes in blocks representable by GMMs which are then merged with the current model estimate, our method works for the case when novel data points arrive one- by-one, while requiring little additional memory. We keep only two GMMs in the memory and no historical data. The current fit is updated with the assumption that the number of components is fixed which is increased (or reduced) when enough evidence for a new component is seen. This is deducedfrom the change from the oldest fit of the same complexity, termed the Historical GMM, the concept of which is central to our method. The performance of the proposed method is demonstrated qualitatively and quantitatively on several synthetic data sets and video sequences of faces acquired in realistic imaging conditions.

Recognising user identity in twitter social networks via text mining

Social networks have become a convenient and effective means of communication in recent years. Many people use social networks to communicate, lead, and manage activities, and express their opinions in supporting or opposing different causes. This has brought forward the issue of verifying the owners of social accounts, in order to eliminate the effect of any fake accounts on the people. This study aims to authenticate the genuine accounts versus fake account using writeprint, which is the writing style biometric. We first extract a set of features using text mining techniques. Then, training of a supervised machine learning algorithm to build the knowledge base is conducted. The recognition procedure starts by extracting the relevant features and then measuring the similarity of the feature vector with respect to all feature vectors in the knowledge base. Then, the most similar vector is identified as the verified account.

Recalling yesterday, predicting tomorrow : revisit South Australia conservation practice for heritage mining places, a practical guide for Malaysia

Mining started in South Australia in the early 1840s with the discovery of silver-lead ore at Glen Osmond in 1841 and later copper in Kapunda in 1842 and Burra in 1845; all these discoveries greatly contributed to South Australia's economic development. South Australia entered the mining era at the time when the Cornish engine was at the peak of its development, and the horizontal engine was beginning its rise in popularity. These South Australian historic mining sites contain extensive extant evidence of technology, innovation and human endeavours that previously went into the mining activity. This paper seeks to critically review items for inclusion on the South Australian State Heritage Register, the character of mining heritage places, enforcement by legislation, interpretation of the heritage assessment and how these practices could guide Malaysia in recognizing industrial heritage as a significant part of its cultural heritage that needs to be conserved, restored and managed as a legacy for present and future generations.

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.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

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.