Visual data mining: integrating machine learning with information visualization

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework.

The development and evaluation of distance learning materials for administrative personnel in a multi-national company

There has been substantial research into the role of distance learning in education. Despite the rise in the popularity and practice of this form of learning in business, there has not been a parallel increase in the amount of research carried out in this field. An extensive investigation was conducted into the entire distance learning system of a multi-national company with particular emphasis on the design, implementation and evaluation of the materials. In addition, the performance and attitudes of trainees were examined. The results of a comparative study indicated that trainees using distance learning had significantly higher test scores than trainees using conventional face-to-face training. The influence of the previous distance learning experience, educational background and selected study environment of trainees was investigated. Trainees with previous experience of distance learning were more likely to complete the course and with significantly higher test scores than trainees with no previous experience. The more advanced the educational background of trainees, the greater the likelihood of their completing the course, although there was no significant difference in the test scores achieved. Trainees preferred to use the materials at home and those opting to study in this environment scored significantly higher than those studying in the office, the study room at work or in a combination of environments. The influence of learning styles (Kolb, 1976) was tested. The results indicated that the convergers had the greatest completion rates and scored significantly higher than trainees with the assimilator, accommodator and diverger learning styles. The attitudes of the trainees, supervisors and trainers were examined using questionnaire, interview and discussion techniques. The findings highlighted the potential problems of lack of awareness and low motivation which could prove to be major obstacles to the success of distance learning in business.

A psychological investigation into the relationship between hemispheric functioning and specific written language difficulties (dyslexia), using pharmaceutical intervention techniques

This thesis attempts a psychological investigation of hemispheric functioning in developmental dyslexia. Previous work using neuropsychological methods with developmental dyslexics is reviewed ,and original work is presented both of a conventional psychometric nature and also utilising a new means of intervention. At the inception of inquiry into dyslexia, comparisons were drawn between developmental dyslexia and acquired alexia, promoting a model of brain damage as the common cause. Subsequent investigators found developmental dyslexics to be neurologically intact, and so an alternative hypothesis was offered, namely that language is abnormally localized (not in the left hemisphere). Research in the last decade, using the advanced techniques of modern neuropsychology, has indicated that developmental dyslexics are probably left hemisphere dominant for language. The development of a new type of pharmaceutical prep~ration (that appears to have a left hemisphere effect) offers an oppertunity to test the experimental hypothesis. This hypothesis propounds that most dyslexics are left hemisphere language dominant, but some of these language related operations are dysfunctioning. The methods utilised are those of psychological assessment of cognitive function, both in a traditional psychometric situation, and with a new form of intervention (Piracetam). The information resulting from intervention will be judged on its therapeutic validity and contribution to the understanding of hemispheric functioning in dyslexics. The experimental studies using conventional psychometric evaluation revealed a dyslexic profile of poor sequencing and name coding ability, with adequate spatial and verbal reasoning skills. Neuropsychological information would tend to suggest that this profile was indicative of adequate right hemsiphere abilities and deficits in some left hemsiphere abilities. When an intervention agent (Piracetam) was used with young adult dyslexics there were improvements in both the rate of acquisition and conservation of verbal learning. An experimental study with dyslexic children revealed that Piracetam appeared to improve reading, writing and sequencing, but did not influence spatial abilities. This would seem to concord with other recent findings, that deve~mental dyslexics may have left hemisphere language localisation, although some of these language related abilities are dysfunctioning.

Transferring knowledge of manufacturing techniques within a subsidiary of a multi-national corporation

With the growth of the multi-national corporation (MNCs) has come the need to understand how parent companies transfer knowledge to, and manage the operations of, their subsidiaries. This is of particular interest to manufacturing companies transferring their operations overseas. Japanese companies in particular have been pioneering in the development of techniques such as Kaizen, and elements of the Toyota Production System (TPS) such as Kanban, which can be useful tools for transferring the ethos of Japanese manufacturing and maintaining quality and control in overseas subsidiaries. Much has been written about the process of transferring Japanese manufacturing techniques but much less is understood about how the subsidiaries themselves – which are required to make use of such techniques – actually acquire and incorporate them into their operations. This research therefore takes the perspective of the subsidiary in examining how knowledge of manufacturing techniques is transferred from the parent company within its surrounding (subsidiary). There is clearly a need to take a practice-based view to understanding how the local managers and operatives incorporate this knowledge into their working practices. A particularly relevant theme is how subsidiaries both replicate and adapt knowledge from parents and the circumstances in which replication or adaptation occurs. However, it is shown that there is a lack of research which takes an in-depth look at these processes from the perspective of the participants themselves. This is particularly important as much knowledge literature argues that knowledge is best viewed as enacted and learned in practice – and therefore transferred in person – rather than by the transfer of abstract and de-contextualised information. What is needed, therefore, is further research which makes an in-depth examination of what happens at the subsidiary level for this transfer process to occur. There is clearly a need to take a practice-based view to understanding how the local managers and operatives incorporate knowledge about manufacturing techniques into their working practices. In depth qualitative research was, therefore, conducted in the subsidiary of a Japanese multinational, Gambatte Corporation, involving three main manufacturing initiatives (or philosophies), namely 'TPS‘, 'TPM‘ and 'TS‘. The case data were derived from 52 in-depth interviews with project members, moderate-participant observations, and documentations and presented and analysed in episodes format. This study contributes to our understanding of knowledge transfer in relation to the approaches and circumstances of adaptation and replication of knowledge within the subsidiary, how the whole process is developed, and also how 'innovation‘ takes place. This study further understood that the process of knowledge transfer could be explained as a process of Reciprocal Provider-Learner Exchange that can be linked to the Experiential Learning Theory.

Visual data mining using principled projection algorithms and information visualization techniques

We introduce a flexible visual data mining framework which combines advanced projection algorithms from the machine learning domain and visual techniques developed in the information visualization domain. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection algorithms, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates and billboarding, to provide a visual data mining framework. Results on a real-life chemoinformatics dataset using GTM are promising and have been analytically compared with the results from the traditional projection methods. It is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework. Copyright 2006 ACM.

Issues in learning an ontology from text

Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. Results - Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/~kiffer/animalbehaviour/ webcite. Conclusion - We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.

Evolution, recurrency and kernels in learning to model inflation

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.