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This group project involves finding and exploring data using Data Mining techniques such as Clustering, ARM, and Classification with algorithms like k-means, Apriori, and Naïve Bayes. The project requires a report and presentation analyzing results, data complexity, preprocessing, and relevance. Tasks include exploring how preprocessing affects results, analyzing clustering outcomes based on pre-processing, and classifying datasets efficiently. Students will need to address attribute nominality, zero values, and labeling for accurate analysis.

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Assignment Help

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  1. Assignment Help

  2. Basic Information • Due date • 5pm Friday 31st May 2019 • Group work • Two maximum (but could allow up to 3 if there are good reasons) • Procedure • Find a data to explore • Explore and find patterns using DM techniques • AT LEAST one DM area and AT LEAST one DM algorithm • Clustering with k-means, DBSCAN or/and hierarchical clustering • ARM with apriori or FP-growth • Classification with kNN, DT, NN, BN etc • 40% total • 30% Report • 10% Presentation

  3. Basic Information • 30% Report (10-15 pages) • Data complexity & preprocessing: 5% • Relevance & appropriateness: 5% • Readability & presentation: 5% • Analysis of results and conclusion: 5% • Scientific & technical quality: 5% • Structure & organisation: 5% • 10% Presentation (5min + 2min Q/A) • Visual aids (2%) • Information communication (2%) • Good eye contact and presentation gestures (2%) • Length of presentation (2%) • Delivery and Q/A (2%)

  4. ARM • Algorithm • Apriori or FP-growth • Things to consider • Ensure all attributes are nominal • Ensure zero (or absence or negative or unimportant) value does not dominate the result • Remove zeros • Demo: BookClub • Possible scenarios • Explore how preprocessing affects the results and report • NumericToBinary • Discretize • Explore multi-level (hierarchical ARM) • Demo with the crime data

  5. Clustering • Algorithm • k-means & DBSCAN & Hierarchical • Possible scenarios • For a dataset with labels • Explore how pre-processing affects the clustering results • Demo: with the iris dataset (k-means vs. cfssubsetevalvs. PCs) • Explore how parameter tuning affects the clustering results • Different number of seeds (the effect of seeds), different k • For a dataset without labels • We don’t know the number of k here • Explore how to choose the best value k using the k-means for a chosen dataset • Use the within cluster sum squared errors (might be k from 1 to 10) • Draw a distribution when the errors drop suddenly • Or explore the dataset to find any interesting patterns

  6. Classification • Algorithm • 1R, J48, Ibk, MultilayerPerceptron, SMO, NaïveBayes • Things to consider • Ensure the dataset has a label attribute • Possible scenarios • Explore how preprocessing affects the results and report • How dimension reduction or attribution selection affects the results and classification efficiency for a certain classifier (or multiple classifiers) • Compare and contrast classification accuracy with various classifiers to see which performs well for a certain dataset and you derive your justifications for why is that?

  7. A Sample Report Structure • Introduction (1 page) • Brief background • Description on Dataset (1-2 pages) • Details about the dataset including how many instances, attributes etc • Preprocessing • Details about the preprocessing done for the dataset including cleaning, transformation etc • DM Area and DM Algorithm • Brief introduction into DM area & algorithm of your choice and provide justifications for the choice • Scenarios (Exploration of the Effect of k in k-means clustering) • Explanation on what you are going to do in your DM project • Results and Analysis • Conclusion • Reference

  8. Basic Information • Due date • 5pm Friday 31stMay 2019 • Presentation • In-class during Week 13 lecture time • What to submit? (through LearnJCU) • Report & data • Power point slide • Scripts/codes you’ve written

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