Chris Bowerman

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%.

PERC: a personal email classifier

Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed.

Combined detection model for criminal network detection

Detecting criminal networks from arrest data and offender demographics data made possible with our previous models such as GDM, OGDM, and SoDM and each of them proved successful on different types of criminal networks. To benefit from all features of police arrest data and offender demographics, a new combined model is developed and called as combined detection model (ComDM). ComDM uses crime location, date and modus operandi similarity as well as surname and hometown similarity to detect criminal networks in crime data. ComDM is tested on two datasets and performed better than other models.

Prediction of past unsolved terrorist attacks

In this study, a novel model is proposed to predict perpetuators of some terrorist events which are remain unsolved. The CPM learns from similarities between terrorist attacks and their crime attributes then puts them in appropriate clusters. Solved and unsolved attacks are gathered in the same - all linked to each other - “umbrella” clusters; then CPM classifies all related terrorist events which are expected to belong to one single terrorist group. The developed model is applied to a real crime dataset, which includes solved and unsolved terrorist attacks and crimes in Turkey between 1970 and 2005. CPM predictions produced significant precision value for big terrorist groups and reasonable recall values for small terrorist groups.

A Resource Reservation Protocol with Linear Traffic Prediction for OBS Networks

This paper addresses the issue of providing resource reservation mechanism for OBS networks. We propose a linear prediction mechanism based on least mean square (LMS) method to reduce the burst delay at edge nodes. A reservation method is proposed to increase the reservation probability and to improve the delay reduction performance.

Comparison of Feature-Based Criminal Network Detection Models with k-Core and n-Clique

Four group detection models (e.g. GDM, OGDM, SoDM and ComDM) are developed based on crime data features. These detection models are compared more common baseline SNA group detection algorithms. It is intended to find out, whether these four crime data specific group detection models can perform better than widely used k-core and n-clique algorithms. Two data sets which contain previously known criminal networks are used as testbeds.

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.

A Fuzzy Taguchi Controller to Improve Genetic Algorithm Parameter Selection

The selection of operators and parameters for genetic algorithms (GA) depends upon the situation, and the choice is usually left to the users. Identifying the optimum selection is very time consuming and, therefore, it is important to develop a system which can assist the users in their selections. In our fuzzy Taguchi controller, we present a hybrid system, which combines the Taguchi method with fuzzy logic, to select near optimum settings for the design parameters. The Taguchi method selects an optimal orthogonal array from experimental design theory, to reduce the number of experiments required to study the parameter space. Our controller uses this array to determine the selection for fuzzy membership in the dynamic selection process. It then applies fuzzy logic to evaluate the beneficial genes which affect the GA performance. We use the hybrid procedure to produce evidence from simulations and this information is then used to refine the GA behaviour. The system utilises a fuzzy matrix to rearrange the sequence of gene groups within the chromosome and applies a fuzzy knowledge base to tune the GA parameter selection. This provides a simple and easy method to assist users to direct their search and optimisation in an efficient way.

The LOM Approach -- A CALL for Concern?.

The LOM (Learning Object Model) approach to courseware design seems to be driven by a desire to increase access to education as well as use technology to enable a higher staff – student ratio than is currently possible. The LOM standard involves the use of standard metadata descriptions of content and adaptive content engines to deliver the conglomerate learning objects to the learner. Whilst there are clear issues of both intellectual property rights as well as appropriate business models (DfES, 2001; HEFCE, 2003), it would also appear that there are a number of other disadvantages. These included the loss of teacher input, the need for a large number of learning objects and different end-user interfaces Our proposed solution, Syntactics, enables teachers to create content and customise materials with only basic IT skills in an environment which offers a common user interface. We argue that such systems, driven by technology, offer many benefits to language teachers and present the CALL community with an opportunity to rethink its strategies.