416 research outputs found
Use of Data Mining in System Development Life Cycle
No full textThis volume provides a snapshot of the current state of the art in data mining, presenting it both in terms of technical developments and industrial applications. The collection of chapters is based on works presented at the Australasian Data Mining conferences and industrial forums. Authors include some of Australia's leading researchers and practitioners in data mining. The volume also contains chapters by regional and international authors. The original papers were initially reviewed for the workshops, conferences and forums.\ud
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The 25 articles in this state-of-the-art survey were carefully reviewed and selected from numerous contributions during at least two rounds of reviewing and improvement for inclusion in the book. They provide an interesting and broad update on current research and development in data mining. The book is divided into two parts. It starts with state-of-the-art research papers organized in topical sections on methodological advances, data linkage, text mining, and temporal and sequence mining. The second part comprises papers on state-of-the-art industrial applications from the fields of health, finance and retail
Assisting Human Cognition in Visual Data Mining
No full textAs discussed in Part 1 of the book in chapter Form-Semantics-Function. A Framework for Designing Visualisation Models for Visual Data Mining the development of consistent visualisation techniques requires systematic approach related to the tasks of the visual data mining process. Chapter Visual discovery of network patterns of interaction between attributes presents a methodology based on viewing visual data mining as a reflection-in-action process. This chapter follows the same perspective and focuses on the subjective bias that may appear in visual data mining. The work is motivated by the fact that visual, though very attractive, means also subjective, and non-experts are often left to utilise visualisation methods (as an understandable alternative to the highly complex statistical approaches) without the ability to understand their applicability and limitations. The chapter presents two strategies addressing the subjective bias: guided cognition and validated cognition, which result in two types of visual data mining techniques: interaction with visual data representations, mediated by statistical techniques, and validation of the hypotheses coming as an output of the visual analysis through another analytics method, respectively
Visual Data Mining: An Introduction and Overview
No full textIn our everyday life we interact with various information media, which present us with facts and opinions, supported with some evidence, based, usually, on condensed information extracted from data. It is common to communicate such condensed information in a visual form - a static or animated, preferably interactive, visualisation. For example, when we watch familiar weather programs on the TV, landscapes with cloud, rain and sun icons and numbers next to them quickly allow us to build a picture about the predicted weather pattern in a region. Playing sequences of such visualisations will easily communicate the dynamics of the weather pattern, based on the large amount of data collected by many thousands of climate sensors and monitors scattered across the globe and on weather satellites. These pictures are fine when one watches the weather on Friday to plan what to do on Sunday - after all if the patterns are wrong there are always alternative ways of enjoying a holiday. Professional decision making would be a rather different scenario. It will require weather forecasts at a high level of granularity and precision, and in real-time. Such requirements translate into requirements for high volume data collection, processing, mining, modelling and communicating the models quickly to the decision makers. Further, the requirements translate into high-performance computing with integrated efficient interactive visualisation. From practical point of view, if a weather pattern can not be depicted fast enough, then it has no value. Recognising the power of the human visual perception system and pattern recognition skills adds another twist to the requirements - data manipulations need to be completed at least an order of magnitude faster than real-time in order to combine them with a variety of highly interactive visualisations, allowing easy remapping of data attributes to the features of the visual metaphor, used to present the data. In this few steps in the weather domain, we have specified some requirements towards a visual data mining system
Visual communication and trust in the health domain
No full textVisual communication and user trust are always challenges in the health domain where conservativeness, precision, and domain knowledge can outweigh the validity of the outcomes in the analytical methods and processes. It is crucial to provide better awareness and understanding to domain experts, model developers, and even patients who might be conscious of their condition and how a treatment or a diagnosis is decided for them. This chapter contributes a discussion on trust and its issues in health data-driven science and how trust should be associated with analytical and computational processes, which are enhanced by visualisation and interaction. We also provide brief guidance on the models and methods for improving interpretability and trust in the health domain
"Inner listening" as a basic principle for developing immersive virtual worlds
No full textLudmil Duridanov and Simeon Simoff call in their paper “'Inner Listening' as a Basic Principle for Developing Immersive Virtual Worlds” for an approach that focuses on visualisation as an important way of analysing a Virtual World. They argued that immersive Virtual Worlds have developed on ad-hoc basis, driven mainly by the need for creating inhabited places for virtual communities and environments for distributed gameplay. The goal of achieving immersion has been mainly pursued using convincing 3D interactive graphics technology and the approaches to design have focused on the visualisation aspects, neglecting the “audio design” and the consistent integration of visual and audio designs. As the collaborative and community-related aspects of these environments are expected to be dominant in the future, the authors argue that there is a clear need to develop deeper underlying principles for the design of these inhabited virtual spaces. They conclude that Virtual Worlds of the future should be places that allow for a creative and enlightened state of mind by their inhabitants. Thereby two sources of wisdom – the Judeo-Islamic and Buddhist tradition – should be explored for establishing the principle of “inner listening” as one of the basic principles for developing immersive Virtual Worlds
Style Recognition using Keyword Analysis
No full textThe primary aim of this research project is to develop a generic framework and methodologies that will enable the augmentation of expert knowledge with knowledge extracted from multimedia sources such as text and pictures, for the purpose of classification and analysis. For evaluation and testing purposes of this research study, a furniture design style domain is selected because it is a common belief that design style is an intangible concept that is difficult to analyze. In this paper, we present the results of the analysis of keywords in the text descriptions of design styles. A simple keyword-based matching technique is used for classification and domain specific dictionaries of keywords are used to reduce the dimensionality of feature space. A comparative evaluation was carried out for this classifier and SVM and decision tree based classifier C4.
Visualisation for explainable machine learning in biomedical data analysis
No full textThis chapter covers innovations in biomedical data mining and interpretations, especially using visualisations in interpretable machine learning for biomedical data analysis. Visualisations are important in presenting artificial intelligence models and validating the machine learning results. There are more new and complex machine learning methods that have been created to assist decision-making in recent years in the medical domain. Most of them are treated as “black boxes”, as the training and prediction processes are hidden in complicated mathematical theories. Visualisation is a way to reveal the process and help a human understand the cause of a decision. Knowing the “why” for the prediction results and “how” the model works can improve users’ trust in artificial intelligence results. The chapter introduces different visualisations used in interpreting supervised and unsupervised machine learning models for biomedical data. We also provide discussions and future work on using visualisations in interpreting data mining results in the medical domain
Feature-ranking methods for RNA sequencing data
No full textRibonucleic acid sequencing (RNA-Seq) is a technique that is used a lot to study and evaluate gene expression patterns and find genes that are expressed differently in different biological situations. Numerous computational algorithms for analysing RNA-seq data have been developed that categorise them per features in many pre-defined classifications. Feature-ranking techniques have emerged as a powerful tool for analysing RNA sequencing data, enabling the identification of the most relevant genes that are associated with specific phenotypes or biological processes. In this chapter, we give an overview of different ways to rank features and how they can be used to analyse data from RNA sequencing. We also compare how well different methods work using benchmark datasets and talk about the difficulties of combining multiple data sources and figuring out what the results mean. Last, we talk about possible future directions for the development and use of feature-ranking techniques. These include the use of deep learning techniques, the use of single-cell sequencing data, and the development of methods for figuring out how genes interact with each other. We evaluate selected features by optimising parameters and identifying a higher-performing classifier. The accuracy, recall, false-positive rate (FPR), and precision are used to analyse the comparison. The chapter aims to provide a comprehensive guide for researchers who want to use feature-ranking techniques to analyse RNA sequencing data and gain insights into the underlying biology
Computational intelligent data analysis for sustainable development : an introduction and overview
No full textThe concept of sustainability received worldwide recognition as a result of a report that was published in 1987 by the World Commission on Environment and Development (known as the Brundtland Commission), titled “Our Common Future”. The commission developed today’s generally accepted definition of sustainability, stating that sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The three main pillars of sustainable development include economic growth, environmental protection, and socio-political sustainability. While many people agree that each of these three ideas contributes to the overall idea of sustainability, it is difficult to find evidence of equal levels of initiatives for the three pillars in governmental policies worldwide
Applying web personalization techniques in e-government services
No full textMany E-commerce websites attempt to develop personalized features to encourage users' repetitive visits. Yet, there is less attention about the applications of personalization technologies in E-government services. In this study, we present a classification of personalization techniques. Also, a novel recommendation approach is proposed to improve the existing techniques by the integration of user-based and item-based collaborative filtering recommendation techniques. A recommender system prototype, named Smart Trade Exhibitions Finder, is developed to help companies choosing the right trade exhibitions. The outcome of this study will have tremendous significance in overcoming the drawback of existing recommendation approaches. © 2005. Xuetao Guo, Jie Lu & Simeon Simoff
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