An incremental FP-growth web content mining and its application in preference identification

This paper presents a real application of Web-content mining using an incremental FP-Growth approach. We firstly restructure the semi-structured data retrieved from the web pages of Chinese car market to fit into the local database, and then employ an incremental algorithm to discover the association rules for the identification of car preference. To find more general regularities, a method of attribute-oriented induction is also utilized to find customer’s consumption preferences. Experimental results show some interesting consumption preference patterns that may be beneficial for the government in making policy to encourage and guide car consumption.

Characteristics and underpricing of Australian mining and energy IPOs from 1994 to 2001

This paper follows How (2000) who examined 130 Australian mining and energy initial public offerings (IPOs) from 1979 to 1990 to report an average 107.18 % underpricing return by those IPOs. This study updates that report by investigating 127 Australian mining and energy IPOs from 1994 to 2001 to find a substantially lower 17.93 % average first day return. These updated findings have implications for both new companies seeking to float and also for the subscribers wishing to invest in these new listings.

Stakeholder and gender characteristics of mining and energy IPO boards of directors

The mining and energy sectors are particularly publicly sensitive sectors and subject to a high degree of public scrutiny. Evan and Freeman (1993) suggest that such public scrutiny needs may be better met by having direct public stakeholder representation on the board of directors. Similarly, Bilimoria (2000) argues a strong commercial case for engaging women on boards. This paper investigates the number and proportion of non equity holding public stakeholder directors and the number and proportion of women directors on the boards of Australian mining and energy company initial public offerings (IPOs) and reports a paucity of public stakeholder directors and also a low proportional female representation on such IPO boards.

Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

Mining and energy law

This text is an ideal starting point to understand the regulatory regimes and policy challenges relevant to Australia's mining sector.

Software warehouse and software mining : the impact of data mining to software engineering

Automating Software Engineering is the dream of software Engineers for decades. To make this dream to come to true, data mining can play an important role. Our recent research has shown that to increase the productivity and to reduce the cost of software development, it is essential to have an effective and efficient mechanism to store, manage and utilize existing software resources, and thus to automate software analysis, testing, evaluation and to make use of existing software for new problems. This paper firstly provides a brief overview of traditional data mining followed by a presentation on data mining in broader sense. Secondly, it presents the idea and the technology of software warehouse as an innovative approach in managing software resources using the idea of data warehouse where software assets are systematically accumulated, deposited, retrieved, packaged, managed and utilized driven by data mining and OLAP technologies. Thirdly, we presented the concepts and technology and their applications of data mining and data matrix including software warehouse to software engineering. The perspectives of the role of software warehouse and software mining in modern software development are addressed. We expect that the results will lead to a streamlined high efficient software development process and enhance the productivity in response to modern challenges of the design and development of software applications.

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.

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.

Software warehouse: its design management and application

To have an effective and efficient mechanism to store, manage and utilize software sources is essential to the automation of software engineering. The paper presents an innovative approach in managing software resources using software warehouse where software assets are systematically accumulated, deposited, retrieved, packaged, managed and utilized, driven by data-mining and OLAP technologies. The results lead to streamlined high efficient software development process and enhance the productivity in response to modern challenges of the design and development of software applications.

Analyzing security protocols using association rule mining

Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.

An web content based data mining for car consumption in China

This paper introduces an incremental FP-Growth approach for Web content based data mining and its application in solving a real world problem The problem is solved in the following ways. Firstly, we obtain the semi-structured data from the Web pages of Chinese car market and structure them and save them in local database. Secondly, we use an incremental FP-Growth algorithm for mining association rules to discover Chinese consumers' car consumption preference. To find more general regularities, an attribute-oriented induction method is also utilized to find customer's consumption preference among a range of car categories. Experimental results have revealed some interesting consumption preferences that are useful for the decision makers to make the policy to encourage and guide car consumption. Although the current data we used may not be the best representative of the actual market in practice, it is still good enough for the decision making purpose in terms of reflecting the real situation of car consumption preference under the two assumptions in the context.

Informing decision-making in libraries: informetric research as input to LIS education and practice.(Library and Information Science).

Findings from informetric research represent an important background resource to add to the mix of information useful for resolving difficult and ongoing problems in specific library environments or information service settings. This paper provides examples of informetric research that can be useful input to decision-making in the field of library management and information service provision. This overview takes four of the challenges that Michael Buckland outlined for library research as a way of guiding the discussion of ways that informetric work can be used to inform library decision-making. (1) References are made to relevant informetric work undertaken or conducted in Australia, by Australian researchers, or with Australian data.

Informetrics includes both quantitative and qualitative methods, which when used in combination can provide a rounded set of findings that has great validity for management, policy and service applications. Quantitative methodologies are generally based on bibliometric techniques, such as mining and analysis of data from various bibliographic and textual databases. Qualitative methods include survey, case study and historical approaches. Used in combination, each set of findings adds richness and other perspectives to an analysis.

The isolation and characterisation of humic acids using a variety of alkaline extractants

The investigation into humic acid; chemistry examined the effect the extraction technique used to isolate humic material from the sediment had on the chemical/structural composition and yield of the acid; compared the various isolation techniques used in the literature and developed an extraction technique which minimises the solubilisation of the heavy metals from the inorganic sediment and, examined the complexation capacity of humic acids derived from a sediment source in relation to the heavy metal content and extraction technique.

Extractive transnational corporations, intrastate conflict and peacemaking : frameworks and best practice

This thesis explores the role of mining and oil transnational corporations in corporate peacemaking. That is, helping to bring together warring parties in intrastate conflict to enable them to conduct peace negotiations and then, supporting these negotiations. Key concerns, and new theory, frameworks and best-practice in corporate peacemaking are proposed.