A rough association rule is applicable for knowledge

[[abstract]]The traditional association rule which should be fixed in order to avoid both that only trivial rules are retained and also that interesting rules are not discarded. In fact, the situations which use the relative comparison to express are more complete than to use the absolute comparison. Through relative comparison we proposes a new approach for mining association rule, which has the ability to handle the uncertainty in the classing process, so that we can reduce information loss and enhance the result of data mining. In this paper, the new approach can be applied in find association rules, which has the ability to handle the uncertainty in the classing process and suitable for all data types.[[conferencetype]]國際[[iscallforpapers]]Y[[conferencelocation]]ShangHai, Chin

Relative Association Rules Based on Rough Set Theory

[[abstract]]The traditional association rule that should be fixed in order to avoid the following: only trivial rules are retained and interesting rules are not discarded. In fact, the situations that use the relative comparison to express are more complete than those that use the absolute comparison. Through relative comparison, we proposes a new approach for mining association rule, which has the ability to handle uncertainty in the classing process, so that we can reduce information loss and enhance the result of data mining. In this paper, the new approach can be applied for finding association rules, which have the ability to handle uncertainty in the classing process, is suitable for interval data types, and help the decision to try to find the relative association rules within the ranking data.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Pathway based microarray analysis based on multi-membership gene regulation

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityRecent developments in automation and novel experimental techniques have led to the accumulation of vast amounts of biological data and the emergence of numerous databases to store the wealth of information. Consequentially, bioinformatics have drawn considerable attention, accompanied by the development of a plethora of tools for the analysis of biological data. DNA microarrays constitute a prominent example of a high-throughput experimental technique that has required substantial contribution of bioinformatics tools. Following its popularity there is an on-going effort to integrate gene expression with other types of data in a common analytical approach. Pathway based microarray analysis seeks to facilitate microarray data in conjunction with biochemical pathway data and look for a coordinated change in the expression of genes constituting a pathway. However, it has been observed that genes in a pathway may show variable expression, with some appearing activated while others repressed. This thesis aims to add some contribution to pathway based microarray analysis and assist the interpretation of such observations, based on the fact that in all organisms a substantial number of genes take part in more than one biochemical pathway. It explores the hypothesis that the expression of such genes represents a net effect of their contribution to all their constituent pathways, applying statistical and data mining approaches. A heuristic search methodology is proposed to manipulate the pathway contribution of genes to follow underlying trends and interpret microarray results centred on pathway behaviour. The methodology is further refined to account for distinct genes encoding enzymes that catalyse the same reaction, and applied to modules, shorter chains of reactions forming sub-networks within pathways. Results based on various datasets are discussed, showing that the methodology is promising and may assist a biologist to decipher the biochemical state of an organism, in experiments where pathways exhibit variable expression.School of Information Systems, Computing and Mathematics, Brunel Universit

Evaluating efficient market hypothesis with stock clustering

This study investigates the validity of Efficient Market Hypothesis (EMH) by taking clusters of firms, generated using Self-Organising Maps (SOMs), and comparing their financial performance. Clusters were generated using 10 different financial variables as inputs to SOMs of different sizes. The effectiveness of the clustering was analysed using Silhouette Width, Davies-Bouldin Index and two Dunn’s Index metrics. The financial performance of the clusters was investigated using equal and value weighted returns and portfolio standard deviation. Market capitalisation was the only variable able to generate statistically significant results – in particular larger firms outperformed their smaller counterparts. It was concluded that this difference could be attributed to the volatile time frame chosen (2007-2012) which resulted in investors favouring larger firms. For future work it is recommended that researchers focus more on pre-processing the inputs, using different clusterin

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining