Interestingness Measures for Multi-Level Association Rules

Association rule mining is one technique that is widely used when querying databases, especially those that are transactional, in order to obtain useful associations or correlations among sets of items. Much work has been done focusing on efficiency, effectiveness and redundancy. There has also been a focusing on the quality of rules from single level datasets with many interestingness measures proposed. However, with multi-level datasets now being common there is a lack of interestingness measures developed for multi-level and cross-level rules. Single level measures do not take into account the hierarchy found in a multi-level dataset. This leaves the Support-Confidence approach,which does not consider the hierarchy anyway and has other drawbacks, as one of the few measures available. In this paper we propose two approaches which measure multi-level association rules to help evaluate their interestingness. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Concise representations for association rules in multi-level datasets

Association rule mining has made many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we firstly propose a definition for redundancy; then we propose a concise representation called Reliable basis for representing non-redundant association rules for both exact rules and approximate rules. An important contribution of this paper is that we propose to use the certainty factor as the criteria to measure the strength of the discovered association rules. With the criteria, we can determine the boundary between redundancy and non-redundancy to ensure eliminating as many redundant rules as possible without reducing the inference capacity of and the belief to the remaining extracted non-redundant rules. We prove that the redundancy elimination based on the proposed Reliable basis does not reduce the belief to the extracted rules. We also prove that all association rules can be deduced from the Reliable basis. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules.

Investigating the use of association rules in improving recommender systems

Recommender systems are widely used online to help users find other products, items etc that they may be interested in based on what is known about that user in their profile. Often however user profiles may be short on information and thus when there is not sufficient knowledge on a user it is difficult for a recommender system to make quality recommendations. This problem is often referred to as the cold-start problem. Here we investigate whether association rules can be used as a source of information to expand a user profile and thus avoid this problem, leading to improved recommendations to users. Our pilot study shows that indeed it is possible to use association rules to improve the performance of a recommender system. This we believe can lead to further work in utilising appropriate association rules to lessen the impact of the cold-start problem.

Generating rules with predicates, terms and variables from the pruned neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.

Changing the rules in use? An examination of social enterprise in local governance

As multi-stakeholder entities that explicitly inhabit both social and economic domains, social enterprises pose new challenges and possibilities for local governance. In this paper, we draw on new institutional theory to examine the ways in which locally-focused social enterprises disrupt path dependencies and rules in use within local government. Rather than examining the more commonly asked question of the influence of the state on social enterprise, our purpose here is to examine the impacts of social enterprise on governmental institutions at the local level. Our discussion is based on a mixed-methods study, including an online survey of 66 local government staff, document analysis, and in-depth interviews with 24 social enterprise practitioners and local government actors working to support social enterprise development in Victoria, Australia. We find that, in some instances, the hybrid nature of social enterprise facilitates ‘joining up’ between different functional areas of local government. Beyond organisational relationships, social enterprise also influences local governance through the reinterpretation and regeneration of institutionalised public spaces.

What shapes player performance in soccer? Empirical findings from a panel analysis

In this article, we investigate the pay-performance relationship of soccer players using individual data from eight seasons of the German soccer league Bundesliga. We find a nonlinear pay-performance relationship, indicating that salary does indeed affect individual performance. The results further show that player performance is affected not only by absolute income level but also by relative income position. An additional analysis of the performance impact of team effects provides evidence of a direct impact of team-mate attributes on individual player performance.

Rethinking corporate governance and law in China : the theories, rules and practices

Economic reforms have transformed China into a modern economy - this requires greater emphasis on regulating markets and governing corporations to ensure economic growth continues. Yet, legal reforms are not as straightforward as transplanting Western models; more modification to suit Chinese political land cultural considerations needs to be incorporated. Likewise privatisation of the telecommuications sector does not mean that government influence in the new corporations cease. This is not necessarily negative as long as safeguards are in place. Plainly further reforms to the law and governance will be needed. Given that Confucian philosophy continues to play a central role in Chinese society and values, developing laws and governance practices from Confucian principles will arguably be appropriate for modern China.

Classifying an opponent’s behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observable environment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.

A global vision system for a robot soccer team

This paper describes the real time global vision system for the robot soccer team the RoboRoos. It has a highly optimised pipeline that includes thresholding, segmenting, colour normalising, object recognition and perspective and lens correction. It has a fast ‘paint’ colour calibration system that can calibrate in any face of the YUV or HSI cube. It also autonomously selects both an appropriate camera gain and colour gains robot regions across the field to achieve colour uniformity. Camera geometry calibration is performed automatically from selection of keypoints on the field. The system achieves a position accuracy of better than 15mm over a 4m × 5.5m field, and orientation accuracy to within 1°. It processes 614 × 480 pixels at 60Hz on a 2.0GHz Pentium 4 microprocessor.

Lateral distortional buckling design rules for back to back LiteSteel beams

A new cold-formed steel beam, known as the LiteSteel Beam (LSB), has the potential to transform the low-rise building industry. The new beam is effectively a channel section with two rectangular hollow flanges and a slender web, and is manufactured using a simultaneous cold-forming and electric resistance welding process. Research into the flexural behaviour of single LSB members showed that the LSBs are susceptible to lateral distortional buckling effects and their moment capacities are significantly reduced for intermediate spans. Build-up LSB sections are expected to improve their flexural capacity and to enhance their applications. They are also likely to mitigate the detrimental effects of lateral distortional buckling observed with single LSB members of intermediate spans. However, the behaviour of build up beams is not well understood. Currently available design rules were found to be inadequate to predict the member moment capacities of back to back LSBs. Therefore a research project based on both experimental and numerical studies was undertaken to investigate the flexural behaviour of back to back LSBs with various longitudinal connection spacings under a uniform moment. New design rules were developed using the moment capacity data obtained using finite element analyses and experimental tests. This paper presents the details of the development of design rules for the back to back LSB sections.

Comprehensive evaluation of driver gaze pattern using fuzzy rules for driver training

The over representation of novice drivers in crashes is alarming. Research indicates that one in five drivers’ crashes within their first year of driving. Driver training is one of the interventions aimed at decreasing the number of crashes that involve young drivers. Currently, there is a need to develop comprehensive driver evaluation system that benefits from the advances in Driver Assistance Systems. Since driving is dependent on fuzzy inputs from the driver (i.e. approximate distance calculation from the other vehicles, approximate assumption of the other vehicle speed), it is necessary that the evaluation system is based on criteria and rules that handles uncertain and fuzzy characteristics of the drive. This paper presents a system that evaluates the data stream acquired from multiple in-vehicle sensors (acquired from Driver Vehicle Environment-DVE) using fuzzy rules and classifies the driving manoeuvres (i.e. overtake, lane change and turn) as low risk or high risk. The fuzzy rules use parameters such as following distance, frequency of mirror checks, gaze depth and scan area, distance with respect to lanes and excessive acceleration or braking during the manoeuvre to assess risk. The fuzzy rules to estimate risk are designed after analysing the selected driving manoeuvres performed by driver trainers. This paper focuses mainly on the difference in gaze pattern for experienced and novice drivers during the selected manoeuvres. Using this system, trainers of novice drivers would be able to empirically evaluate and give feedback to the novice drivers regarding their driving behaviour.