Predicate based association rules mining with new interestingness measure

Association Rule Mining (ARM) is one of the fundamental components in the field of data mining that discovers frequent itemsets and interesting relationships for predicting the associative and correlative behaviours for new data. However, traditional ARM techniques are based on support-confidence that discovers interesting association rules (ARs) using predefined minimum support (minsupp) and minimum confidence (minconf) threshold. In addition, traditional AR techniques only consider frequent items while ignoring rare ones. Thus, a new parameter-less predicated based ARM technique was proposed to address these limitations, which was enhanced to handle the frequent and rare items at the same time. Furthermore, a new interestingness measure, called g measure, was developed to select only highly interesting rules. In this proposed technique, interesting combinations were firstly selected by considering both the frequent and the rare items from a dataset. They were then mapped to the pseudo implications using predefined logical conditions. Later, inference rules were used to validate the pseudo-implications to discover rules within the set of mapped pseudo-implications. The resultant set of interesting rules was then referred to as the predicate based association rules. Zoo, breast cancer, and car evaluation datasets were used for conducting experiments. The results of the experiments were evaluated by its comparison with various classification techniques, traditional ARM technique and the coherent rule mining technique. The predicate-based rule mining approach gained an accuracy of 93.33%. In addition, the results of the g measure were compared with a state-of-the-art interestingness measure developed for a coherent rule mining technique called the h value. Predicate rules were discovered with an average confidence value of 0.754 for the zoo dataset and 0.949 for the breast cancer dataset, while the average confidence of the predicate rules found from the car evaluation dataset was 0.582. Results of this study showed that a set of interesting and highly reliable rules were discovered, including frequent, rare and negative association rules that have a higher confidence value. This research resulted in designing a methodology in rule mining which does not rely on the minsupp and minconf threshold. Also, a complete set of association rules are discovered by the proposed technique. Finally, the interestingness measure property for the selection of combinations from datasets makes it possible to reduce the exponential searching of the rules

Sex determination and genetic management in Nile tilapia using genomic techniques

The PhD research studied two aspects in tilapia, firstly the analysis of sex determination in Nile tilapia (evidence of complex sex-determining systems) and secondly the genetic management of the tilapia species, using different genomic analysis approaches. This research started with the development of two techniques: minimally invasive DNA sampling from fish mucus, which was found to be suitable for standard genotyping and double-digest restriction-site associated DNA sequencing – ddRADseq; and pre-extraction pooling of tissue samples for ddRADseq (BSA-ddRADseq), which was found to be suitable for identifying a locus linked to a trait of interest (sex in this case). The first molecular evidence concerning the sex determination in genetically improved farmed tilapia (GIFT) was described using BSA-ddRADseq. Given the multiple stock origin of GIFT, surprisingly only a single locus (in linkage group 23) was found to be associated with the phenotypic sex across the population. The first evidence of LG23 influence on phenotypic sex in the Stirling population of Nile tilapia was also found. Different combinations of estrogen hormones and high temperature were tested for feminising Nile tilapia: a combined treatment of estrogen hormone and high temperature was found to be more efficient in feminising Nile tilapia than the estrogen alone. A set of species-diagnostic SNP markers were tested which were found to be suitable to distinguish pure species (O. niloticus, O. mossambicus and O. aureus), and these were used to analyse species contribution to GIFT and a selected tilapia hybrid strain. The results of the current research added novel information to our understanding of sex determination in Nile tilapia, which will be helpful in the development of marker-assisted selection in GIFT and other Nile tilapia strains towards the production of all male offspring. The methods developed also have broader applicability in genetic and genomics research