James Malone

Data mining using rule extraction from Kohonen self-organising maps

The Kohonen self-organising feature map (SOM) has several important properties that can be used within the data mining/knowledge discovery and exploratory data analysis process. A key characteristic of the SOM is its topology preserving ability to map a multi-dimensional input into a two-dimensional form. This feature is used for classification and clustering of data. However, a great deal of effort is still required to interpret the cluster boundaries. In this paper we present a technique which can be used to extract propositional IF..THEN type rules from the SOM network’s internal parameters. Such extracted rules can provide a human understandable description of the discovered clusters.

Automated trend analysis of proteomics data using an intelligent data mining architecture

Proteomics is a field dedicated to the analysis and identification of proteins within an organism. Within proteomics, two-dimensional electrophoresis (2-DE) is currently unrivalled as a technique to separate and analyse proteins from tissue samples. The analysis of post-experimental data produced from this technique has been identified as an important step within this overall process. Some of the long-term aims of this analysis are to identify targets for drug discovery and proteins associated with specific organism states. The large quantities of high-dimensional data produced from such experimentation requires expertise to analyse, which results in a processing bottleneck, limiting the potential of this approach. We present an intelligent data mining architecture that incorporates both data-driven and goal-driven strategies and is able to accommodate the spatial and temporal elements of the dataset under analysis. The architecture is able to automatically classify interesting proteins with a low number of false positives and false negatives. Using a data mining technique to detect variance within the data before classification offers performance advantages over other statistical variance techniques in the order of between 16 and 46%.

Semantic-Based Workflow Composition for Video Processing in the Grid

We outline the problem of automatic video processing for the EcoGrid. This poses many challenges as there is a vast amount of raw data that need to be analysed effectively and efficiently. Furthermore, ecological data are subject to environmental changes and are exception-prone, hence their qualities vary. As manual processing by humans can be time and labour intensive, video and image processing tools can go some way to addressing such problems since they are computationally fast. However, most video analyses that utilise a combination of these tools are still done manually. We propose a semantic-based hybrid workflow composition method that strives to provide automation to speed up this process. The requirements for such a system are presented, whereby we aim for a solution that best satisfies these requirements and that overcomes the limitations of existing grid workflow composition systems

Analysis of Rules Discovered by the Data Mining Process

This paper describes how symbolic rules may be extracted from Radial Basis Function neural networks and shows how they can be used by the data mining and knowledge discovery process. Rule extraction overcomes a major disadvantage of neural networks which is concerned with making the comprehensibilty of the learned internal model more open to scrutiny. Having extracted the symbolic rules we show how they are assessed and ranked for interesting or novel features. Two such techniques are presented here, the first is a data driven approach that uses objective mathematical measures to identify interesting patterns or features. The second is a goal driven method that uses subjective measures obtained from the user. The measures are applied to rules extracted from RBF neural networks trained on several data sets including benchmark sets from the UCI repository and a large real-world industrial data set.

Spatio-temporal neural data mining architecture in learning robots

There has been little research into the use of hybrid neural data mining to improve robot performance or enhance their capability. This paper presents a novel neural data mining technique that analyses robot sensor data for imitation learning. Learning by imitation allows a robot to learn from observing either another robot or a human to gain skills, understand the behavior of others and create solutions to problems. We demonstrate a hybrid approach of differential ratio data mining to perform analysis on spatio-temporal robot behavioral data. The technique offers classification performance gains for recognition of robot actions by highlighting points of covariance and hence interest within the data.

Soft Computing in Bioinformatics: Genomic and Proteomic Applications

Bioinformatics has been described as the science of managing, mining, and interpreting information from biological sequences and structures (Li et al 2004). The emergence of the field has been largely attributed to the increasing amount of biomedical data created and collected and the availability and advancement of high-throughput experimental techniques. One recent example of this is the advancement of ‘lab-on-achip’ (see Figure 1) technology which allows experimentation to be performed more rapidly and at lower cost, whilst introducing the possibility of observing new phenomena or obtaining more detailed information from biologically active systems (Whitesides 2006). Such advances enable scientists to conduct experiments which result in large amounts of experimental data over a relatively short period of time. The need to analyse such experimental data has often necessitated a similarly highthroughput approach in order to produce rapid results, employing the use of efficient and flexible analysis methods and, in many areas, driving the need for every improving data analysis techniques. It is for this reason bioinformatics draws upon fields including, but not limited to, computer science, biology (including biochemistry), mathematics, statistics and physics.

Semantic Grid Services for Video Analysis

Employing the power of semantic grid services into pervasive problem domains such as video analysis would allow for more effective distributed processing. A vast amount of ecological data from the EcoGrid of varying qualities and features will need to be analysed efficiently. As manual processing by humans can be time and labour intensive, video and image processing tools can go some way to addressing such problems since they are computationally fast. However, most video analyses that utilise a combination of these tools are still done manually. We propose a semantic-based hybrid workflow composition method that strives to provide automation to speed up this process. The main components of this framework are presented, along with the illustration of a scenario where these components act as semantic grid services for EcoGrid video analysis