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.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

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.

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 chapter we propose two approaches which measure multi-level association rules to help evaluate their interestingness by considering the database’s underlying taxonomy. These measures of diversity and peculiarity can be used to help identify those rules from multi-level datasets that are potentially useful.

Multi-level order, friction and contradiction : the evolution of Australian film industry policy

Cultural policy studies have previously highlighted the importance of multiple logics, friction and contradiction in cultural policy. Recent developments in institutional theory provide a framework for analysing change in cultural policy which explores movement between these multiple and sometimes contradictory logics. This paper analyses the role of friction in the evolution of Australian film industry policy and in particular the tension between competing logics regarding nationalism, commercialism and the state. The paper is suggestive of the relevance of institutional theory as a framework for understanding cultural policy evolution.

Self-commutated current source converter with multi-level current reinjection

The multi-level current reinjection concept described in literature is well-known to produce high quality AC current waveforms in high power and high voltage self-commutating current source converters. This paper proposes a novel reinjection circuitry which is capable of producing a 7-level reinjection current. It is shown that this reinjection current effectively increases the pulse number of the converter to 72. The use of PSCAD/EMTDC simulation validates the functionality of the proposed concept illustrating its effectiveness on both AC and DC sides of the converter.

Post-tensioning and its effect on multi-level formwork load distribution

Multi-level concrete buildings requrre substantial temporary formwork structures to support the slabs during construction. The primary function of this formwork is to safely disperse the applied loads so that the slab being constructed, or the portion of the permanent structure already constructed, is not overloaded. Multi-level formwork is a procedure in which a limited number of formwork and shoring sets are cycled up the building as construction progresses. In this process, each new slab is supported by a number of lower level slabs. The new slab load is, essentially, distributed to these supporting slabs in direct proportion to their relative stiffness. When a slab is post-tensioned using draped tendons, slab lift occurs as a portion of the slab self-weight is balanced. The formwork and shores supporting that slab are unloaded by an amount equivalent to the load balanced by the post-tensioning. This produces a load distribution inherently different from that of a conventionally reinforced slab. Through , theoretical modelling and extensive on-site shore load measurement, this research examines the effects of post-tensioning on multilevel formwork load distribution. The research demonstrates that the load distribution process for post-tensioned slabs allows for improvements to current construction practice. These enhancements include a shortening of the construction period; an improvement in the safety of multi-level form work operations; and a reduction in the quantity of form work materials required for a project. These enhancements are achieved through the general improvement in safety offered by post-tensioning during the various formwork operations. The research demonstrates that there is generally a significant improvement in the factors of safety over those for conventionally reinforced slabs. This improvement in the factor of safety occurs at all stages of the multi-level formwork operation. The general improvement in the factors of safety with post-tensioned slabs allows for a shortening of the slab construction cycle time. Further, the low level of load redistribution that occurs during the stripping operations makes post-tensioned slabs ideally suited to reshoring procedures. Provided the overall number of interconnected levels remains unaltered, it is possible to increase the number of reshored levels while reducing the number of undisturbed shoring levels without altering the factors of safety, thereby, reducing the overall quantity of formwork and shoring materials.

Knowledge-based development for cities and societies : integrated multi-level approaches

Over the past twenty years, the conventional knowledge management approach has evolved into a strategic management approach that has found applications and opportunities outside of business, in society at large, through education, urban development, governance, and healthcare, amongst others. Knowledge-Based Development for Cities and Socieities: Integrated Multi-Level Approaches enlightens the concepts and challenges of knowledge management for both urban environments and entire regions, enhancing the expertise and knowledge of scholars, resdearchers, practitioners, managers and urban developers in the development of successful knowledge-based development policies, creation of knowledte cities and prosperous knowledge societies. This reference creates large knowledge base for scholars, managers and urban developers and increases the awareness of the role of knowledge cities and knowledge socieiteis in the knowledge era, as well as of the challenges and opportunities for future research.

Charting the system : the integrated master schedule as a multi-level and poly-temporal boundary object in complex projects

This paper investigates the use of visual artifacts to represent a complex adaptive system (CAS). The integrated master schedule (IMS) is one of those visuals widely used in complex projects for scheduling, budgeting, and project management. In this paper, we discuss how the IMS outperforms the traditional timelines and acts as a ‘multi-level and poly-temporal boundary object’ that visually represents the CAS. We report the findings of a case study project on the way the IMS mapped interactions, interdependencies, constraints and fractal patterns in a complex project. Finally, we discuss how the IMS was utilised as a complex boundary object by eliciting commitment and development of shared mental models, and facilitating negotiation through the layers of multiple interpretations from stakeholders.

A multi-level model of leadership in complex project management

Management (or perceived mismanagement) of large-scale, complex projects poses special problems and often results in spectacular failures, cost overruns, time blowouts and stakeholder dissatisfaction. While traditional project management responds with increasingly administrative constraints, we argue that leaders of such projects also need to display adaptive and enabling behaviours to foster adaptive processes, such as opportunity recognition, which requires an interaction of cognitive and affective processes of individual, project, and team leader attributes and behaviours. At the core of this model we propose is an interaction of cognitive flexibility, affect and emotional intelligence. The result of this interaction is enhanced leader opportunity recognition that, in turn, facilitates multilevel outcomes.

Multi-method, multi-theoretical, multi-level research in the learning sciences

We examine methodologies and methods that apply to multi-level research in the learning sciences. In so doing we describe how multiple theoretical frameworks informs the use of different methods that apply to social levels involving space-time relationships that are not accessible consciously as social life is enacted. Most of the methods involve analyses of video and audio files. Within a framework of interpretive research we present a methodology of event-oriented social science, which employs video ethnography, narrative, conversation analysis, prosody analysis, and facial expression analysis. We illustrate multi-method research in an examination of the role of emotions in teaching and learning. Conversation and prosody analyses augment facial expression analysis and ethnography. We conclude with an exploration of ways in which multi-level studies can be complemented with neural level analyses.