Compare and contrast various data mining software applications. Identify the steps in the decision-making process. Identify the steps in the data mining process. Explain the different data mining techniques.

MIS 685-50 Data Mining Tools

Learning Objectives & Assignment Work

Week 4 Topic: Data Mining Software, Techniques and Applications: Clustering

Course Learning Objectives:

1. Compare and contrast various data mining software applications.
2. Identify the steps in the decision-making process.
3. Identify the steps in the data mining process.
4. Explain the different data mining techniques.
5. Evaluate a data set for ethical considerations.
6. Apply the data mining steps to an association technique using market basket analysis.
7. Prepare a report that explains an association technique output.

References for doing your research – Reading and understanding the concepts.

1. Cluster Analysis in Data Mining
2. Why Use Clustering in Data Mining
3. Clustering Data Mining Techniques: 5 Critical Algorithms 2023
4. Understanding Clustering in Unsupervised Learning
5. K-Means Clustering: Explain It To Me Like I’m 10
6. Clustering With K-Means
7. What is K Means Clustering? With an Example

Assignment: Due by 04/10/2023 (Monday), 9 PM CT; carries 15% of your grade.

1. In about 400 words, define what clustering analysis means and how is this used in the data mining process. (3 Points)
2. In about 400 words, explain the concept of supervised and unsupervised learning. Illustrate using examples . (3 Points)
3. In your own words (about 500), define what K-Clustering means. Based on your understanding of this concept, provide a practical example of where and how this concept is used.  For example, in the field of marketing, finance, sports, etc.  Think of events in your own life, where this concept might have used, be creative (6 Points)