Data Mining

Task 2: Choose one from the following three tasks. a) Mining association rules over distributed databases Review the algorithms mining association rules over distributed databases. b) Mining classification over large databases Review the algorithms mining classification over large databases (focusing on efficiency and scalability). c) Mining cluster over large databases Review algorithms mining cluster over large databases (focusing on performance. e.g. efficiency, scalability, able to deal with noise and outliers). Task 3: A database in .ARFF format has been provided for you on Studynet. Analyse this database using the WEKA toolkit and tools introduced within this module. Produce a report explaining which tools you used and why, what results you obtained, and what this tells you about the data. Marks will be awarded for: variety of tools used, quality of analysis, and interpretation of the results. An extensive report is not required (approx 2000 words), nor is detailed explanation of the techniques employed, but any graphs or tables produced should be described and analysed in the text. A reasonable report could be achieved by doing a thorough analysis using three techniques. An excellent report would use at least four tools to analyse the dataset, and provide detailed comparisons between the results. You should perform the following steps: 1. Analyse the attributes in the data, and consider their relative importance with respect to the target class. 2. Construct graphs of classification performance against training set size for a range of classifiers taken from those considered in the module. 3. Analyse the data structure/representation generated by each classifier using cross validation with various numbers of folds. 4. Combine the results from the previous three steps and all your classifiers to develop a model of why instances fall into particular classes. Produce a report containing your answers to the above.