Assessment item 3 Assessment Item 3 Value: 25% Due date: 30-Sep-2016 Return date: 26-Oct-2016 Submission method options Alternative submission method Task Task 1 (20 marks) Load the Ionosphere.arff dataset in Weka to perform rule classification using the following methods • JRip • Ridor For each method produce a summary of the rules produced and comment on their accuracy using performance metrics used in Weka. Task 2 (20 marks) Load the Vote.arff dataset in Weka to perform association rule learning using the following methods • Apriori • FPGrowth For each method produce a summary of the rules produced and comment on their accuracy. Task 3 (20 marks) Load Breast-Cancer.arff dataset in WEKA for Bayesian classification using the following methods • BayesNet • AODE For each method produce a summary of the classification produced and comment on the accuracy of the method. Rationale To gain understanding of main methods for • Rules classification • Association rule learning • Rule creation using Bayesian Analysis Marking criteria Task 1 (20 marks) Questions HD DI CR PS FL JRip (10 marks) The student has thoroughly understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification method as applied to the given data set The student has understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates detailed understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. The student has not fully understood the classification method, providing a description of the method and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. Ridor (10 marks) The student has thoroughly understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification method as applied to the given data set The student has understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates detailed understanding of the classification method as applied to the given data set The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. The student has not fully understood the classification method, providing a description of the method and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. Task 2 (20 marks) Questions HD DI CR PS FL Apriori (10 marks) The student has thoroughly understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates detailed understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. The student has not fully understood the classification method, providing a description of the method and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. FPGrowth (10 marks) The student has thoroughly understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates detailed understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. The student has not fully understood the classification method, providing a description of the method and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. Task 3 (20 marks) Question HD DI CR PS FL AODE (10 marks) The student has thoroughly understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates detailed understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. The student has not fully understood the classification method, providing a description of the method and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. BayesNet(10 marks) The student has thoroughly understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates thorough understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a detailed description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates detailed understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model demonstrates understanding of the classification method as applied to the given data set. The student has understood the classification method, providing a description of the method and its output on the given data set. The discussion involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. The student has not fully understood the classification method, providing a description of the method and its output on the given data set. The discussion is not fully involving the validation and accuracy of the model shows basic understanding of the classification method as applied to the given data set. Presentation To gain understanding of main methods for • Rules classification • Association rule learning • Rule creation using Bayesian Analysis