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7BUIS008W Data Mining and Machine LearningUniversity ofFaculty of Sciences and Technologies- Dept. of ComputerScienceAssessment Type: Individual coursework, CWK1Examiner: DrSession: 2016-2017Semester: OneAssessment weight: 50% of the overall module markDate Set: 18/10/2016Due Date: 08/11/2016 at 10:00 amFeedback Date: 23/11/2016Learning OutcomesLO1 critically justify the use of eFective and novel data mining and machine learningtechniques for Data Science applications;LO3 critically re±ect on the knowledge of how diFerent data mining and machinelearning algorithms operate and their underlying design assumptions and biases in orderto select and apply an appropriate such algorithms to solve a given problem;LO5 critically analyse the output of data mining and machine learning algorithms bydrawing technically appropriate and justi²able conclusions resulting from the applicationof data mining and machine learning algorithms to real-world data sets.DescriptionOne of the research activities in the area of association rule analysis is ondevelopment of eFcient and scalable mining algorithms. Association rulelearning is a method for discovering interesting relations betweenvariables in large/big databases. It is intended to identify strong rulesdiscovered in databases using some measures of interestingness. Suchinformation can be used as the basis for decisions about marketingactivities such as, e.g., promotional pricing or product placements.Association rules mining is employed today in many application areasincluding Web usage mining, intrusion detection, Continuous production,and bioinformatics.TaskYou are required to critically analyse and report 3di±erent data miningalgorithms of your own choice for extracting association rules out of adata repository. Your technical report must include the followingdeliverables (dev’s1-5).Dev1: Short description of the algorithms. Provide the main steps ofeach algorithm on the form of i.e pseudo-code etc.[8 Marks]Dev2: With the aid of a sample and short example demonstrate theprocessfrequentitemsetgeneration;includingrejectionofinfrequent items.[15 Marks]Dev3: Based on the output of the frequent itemset generationprocess, demonstrate the extraction of association rules.[7 Marks]

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