Data Mining Module code: 7COM1018 Module
Data Mining Module
School of Physics, Engineering and Computer ScienceAssignment Briefing Sheet (2020/21 Academic Year)Section A: Assignment title, important dates and weightingAssignment title: Practical Group orindividual:IndividualModule title: Data Mining Modulecode:7COM1018Module leader: Peter Lane Moderator’sinitials:Submissiondeadline:6th January 202123:59Target date for return ofmarked assignment:3rd February 2021You are expected to spend about 40 hours to complete this assignment to asatisfactory standard.This assignment is worth 40% of the overall assessment for this module. Section B: Student(s) to complete Student ID numberYear CodeNOT NEEDED FOR ONLINE SUBMISSION Notes for students For undergraduate modules, a score above 40% represent a pass performance at honours level. See another question tackled by our nursing writing experts on prevention and control program
For postgraduate modules, a score of 50% or above represents a pass mark. Late submission of any item of coursework for each day or part thereof (or for hard copy submissiononly, working day or part thereof) for up to five days after the published deadline, coursework relatingto modules at Levels 0, 4, 5, 6 submitted late (including deferred coursework, but with the exception ofreferred coursework), will have the numeric grade reduced by 10 grade points until or unless thenumeric grade reaches or is 40. Where the numeric grade awarded for the assessment is less than40, no lateness penalty will be applied. Late submission of referred coursework will automatically be awarded a grade of zero (0). Coursework (including deferred coursework) submitted later than five days (five working days in thecase of hard copy submission) after the published deadline will be awarded a grade of zero (0). Regulations governing assessment offences including Plagiarism and Collusion are available fromhttps://www.herts.ac.uk/about-us/governance/university-policies-and-regulations-uprs/uprs (pleaserefer to UPR AS14) Guidance on avoiding plagiarism can be found here:https://herts.instructure.com/courses/61421/pages/referencing-avoiding-plagiarism?module_item_id=779436 Modules may have several components of assessment and may require a pass in all elements. Forfurther details, please consult the relevant Module Handbook (available on Studynet/Canvas, underModule Information) or ask the Module Leader.Page 1 of 3School of Physics, Engineering and Computer ScienceAssignment Briefing Sheet (2020/21 Academic Year) This Assignment assesses the following module Learning Outcomes (from Definitive ModuleDocument):Successful students will typically:2. be able to appreciate the strengths and limitations of various data mining models;3. be able to critically evaluate, articulate and utilise a range of techniques for designing data miningsystems;5. be able to critically evaluate different algorithms and models of data mining.
Assignment Brief: A dataset of text is provided in the assignment area on Canvas. Analyse this data using the WEKAtoolkit and tools introduced within this module, comparing two different forms of preprocessing: Forexample, you may investigate the impact of using stemming, the effect of reducing the number offeatures, the impact of term frequency over a simple word count, etc.Complete the following tasks:1. Describe which question you will be investigating (e.g. “is stemming beneficial to improvingperformance?”, “is the reduction of features beneficial to improving performance?”, etc.)2. Convert the text dataset into TWO different databases in ARFF format, based on your chosenquestion. Explain the conversion techniques and parameters that you have used, along with anyother pre-processing you wish to do. (Do not include a screen shot of the attributes in WEKA –you need to describe them.)3. For each database, produce a table and a graph of classification performance against trainingset size for the following three classifiers: decision-tree (J48), Naïve Bayes, Support VectorMachine. For the Support-Vector Machine you must determine the kernel,and its parameters.4. Write a conclusion. You should at least compare the performance of the different learningalgorithms on your databases, and answer the question you posed in part (1).Remember to explain the steps you have taken to complete each task in your report. Screenshotsare typically not required, and should be used sparingly if at all.Submission Requirements:A single PDF document containing your report, to a maximum 10 pages.Marks awarded for:Marks will be awarded out of 100 in the proportion:1. Question (5 marks)2. Conversion (40 marks)3. Training/testing (40 marks)4. Conclusion (15 marks)A reminder that all work should be your own. Reports exceeding the maximum length may notbe marked beyond the 10 pages.Type of Feedback to be given for this assignment:Along with the marks, each student will receive individual written feedback on the online platform. Page 2 of 3School of Physics, Engineering and Computer SciencePage 3 of 3
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