Data Mining Algorithms and Techniques - IT Assignment Help

Assignment Task:

The objective of the Assignment

To successfully apply a set of data mining skills imparted through lectures and lab session to a previously unseen dataset using Weka to achieve knowledge discovery and producing a written technical paper format report.

Deliverables

A single zip called FirstName_LastName_StudentNumber._ass1.zip to be uploaded to Moodle containing the following files

• This file edited to contain the results of your investigation. Each of the NUMBERED headings should be expanded to satisfy the requirements of the section. 

• A set of supporting files including but not limited to the following, which should be clearly referenced from your documentation.

  • dataset.arff

  • trainigSet.arff

  • testingSet.arff

  • j48tree.arff

  • associationrules.arff

  • kmeans.arff

  • dbscan.arff

Choosing Your Dataset 

1. Your dataset should concern a real-world problem that lends itself to easy understanding by your classmates. 

2. It should ideally have >1000 tuples/rows/instances.

3. It should ideally have >=6 attributes 

4. It should have attributes which can serve as labels so that the accuracy of your data analysis can be determined. 

5. If you cannot find one dataset which is suitable for use with all techniques, then you may choose 2. Please clearly indicate which dataset was used in which case and introduce this dataset

Part 1 – Classification

1. Description of your dataset and findings

• Title: Brief title to capture the data and objective of your assignment 

• Objective: What you want to uncover by examining the data in this assignment. You can update this as you progress through your project revising it and making it more specific. 

• Data description: A description of the data in detail under the following subheadings: 

  • The problem domain

  • The source of the data

  • The agencies working with the data

  • The intended use of the data

  • The attribute types of data

Please include screenshots (with one or two sentences of summary) of the dataset and also of the data summaries and graphs that are available through Weka. 

• Summary of Findings: This should feature here at the top of the document, but be written following the application of your data mining techniques. Should contain numerical values and discussion.

2. Preprocessing

In this section, you should 

1. Identify the set of preprocessing techniques that can be applied to your data and clearly indicate which techniques are appropriate and which ones are not. 

2. Provide evidence through a screenshot of the effects of preprocessing the data along with a short explanation. 

3. Generate a file called dataset.arff which is the outcome of the preprocessing. 

3. Divide your dataset into training and test set

Divide the dataset into training and testing data sets (9:1). Additional resources links are in moodle.  The files generated as part of this process should be saved and submitted as the following

• trainingSet.arff and 

• testingSet.arff

Screenshots of these files should be included.

Experiments

For each of the following classification techniques

1. Train your model using trainingSet.arff 

2. Test your model using testingSet.arff

3. Write a few paragraphs analyzing the results. Be sure to vary parameters at least 3 times in each case. Support this analysis with screenshots of the following 

   1. The model or a visualization of the model

   2. The results of the model 

   3. Any additional output of the model including but not limited to 

      1. Rules 

      2. Confidence Values

      3. Confusion Matrixes

      4. Etc.

   4. Simple references to the notes or URL links to online resources complete with a sentence or two of explanation.

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