Aispire Consulting Ltd.

Aberystwyth, United Kingdom

Aispire Consulting Ltd.

Aberystwyth, United Kingdom
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Iam-On N.,Mae Fah Luang University | Garrett S.,Aispire Consulting Ltd. | Price C.,Aberystwyth University
International Journal of Data Mining and Bioinformatics | Year: 2013

Clinical data has been employed as the major factor for traditional cancer prognosis. However, this classic approach may be ineffective for analysing morphologically indistinguishable tumour subtypes. As such, microarray technology emerges as the promising alternative. Despite a large number of microarray studies, the actual clinical application of gene expression data analysis remains limited owing to the complexity of generated data and the noise level. Recently, the integrative cluster analysis of both clinical and gene expression data has been shown to be an effective alternative to overcome the above-mentioned problems. This paper presents a novel method for using cluster ensembles that is accurate for analysing heterogeneous biological data. Evaluation against real biological and benchmark data sets suggests that the quality of the proposed model is higher than many state-of-the-art cluster ensemble techniques and standard clustering algorithms. Copyright © 2013 Inderscience Enterprises Ltd.

Iam-On N.,Mae Fah Luang University | Boongoen T.,Royal Thai Air Force Academy | Garrett S.,Aispire Consulting Ltd. | Price C.,Aberystwyth University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2011

Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations, while those among clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over 10 data sets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and regularly illustrate higher clustering quality in comparison to several state-of-the-art techniques. © 2011 IEEE.

Iam-On N.,Mae Fah Luang University | Boongeon T.,Royal Thai Air Force Academy | Garrett S.,Aispire Consulting Ltd. | Price C.,Aberystwyth University
IEEE Transactions on Knowledge and Data Engineering | Year: 2012

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques. © 2006 IEEE.

Harding C.,Aberystwyth University | Garrett S.,Aispire Consulting Ltd | Wang S.,Aberystwyth University
Information and Communications Technology Law | Year: 2015

The use of hypothetical factual situations to explore and discuss the way in which ‘real-life’ events and problems occur and develop is an established and valuable predictive method for understanding such events and testing the resolution of problems. In so far as such simulation employs hypothetical events and itineraries of action as a way of arriving at conclusions and solutions, it may be described as a kind of game-playing, and the discussion in this paper first explores the value of simulation exercises in legal and other contexts. Then, taking the example of a simulation based upon a board game involving strategies and risks arising in the legal regulation of business cartels, it reports on the testing of a more powerful computerised version of the board game. Examining the outcome of a large number of moves around the game board, this served as a pilot study for considering the value of further development of such a computational model for possible application in research, educational, and training contexts. © 2015 Taylor & Francis

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