Unsupervised Learning is a machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabelled data. Unsupervised learning problems can be further grouped into clustering and association problems (for examples: Audience/customer segmentation, Pattern recognitign, medical diagnosis, ) In this context you are requested to carry out a project with the following 7 phases:
Phase 1: Business Understanding (2 marks)
Provide the nature of your project: Determine the scope of the business problem and objectives. Describe what your project is about include whether you will be performing data mining tasks, or modifying some other system to incorporate data mining features, etc. It is critical that your problem is well-defined.
Phase 2: Data understanding and preparation (4 marks)
Explore and collect data that will help solve the stated business problem. Prepare the data for further modeling procedures. Include the origin of the data set, an overview of the data set organization, attributes of the data, and challenges of the data set you've selected.
Phase 3:Data Mining Task (2 marks)
Provide the specific tasks you will perform on the data set. Include specific questions you will investigate, and the goals for the tasks. This should be independent of the specific techniques you will use to achieve your goals.
Phase 4: Methods and Models (unsupervised data mining techniques) (8 marks, 4 marks For each technique)
In order to achieve the goals, you set in the data mining task section & find valuable and hidden knowledge from data you need to apply two unsupervised data mining techniques (Clustering and Association) based on the type of dataset you are dealing with and your objectives (for example vou may apply. K-Means and Apriori Algorithms).
You may use data mining packages (e.g. WEKA). Or implement the data mining algorithms yourself, in any programming language. Make clear in your report what existing software you are using.
Phase 5: Compare models and assess results (4 marks)
Compare the efficiency of the techniques used in phase 4, draw conclusions from the data models and assess their validity. Translate the results into a business decision and mention how they help the organization to improve decision-making processes and gain competitive advantage.
Phase 6: Presentation and Visualization (3 marks)
Use data visualization tools and techniques to present and interpret the data mining results in such a way to show meaningful patterns in the data.
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