Data representation and association analysis

Q1. Read: Ch. 6 &7 in the textbook: Data Representation and Interactivity

Textbook: Kirk, Andy.  Data Visualization: A Handbook for Data Driven Design

Data representation and interactivity are important aspects of data visualization.  Review the text by Kirk (2019) and note at least three storytelling techniques.  Note the importance of each and the advantages of using these techniques.

Q2. Read Ch. 6 in the textbook. Association Analysis: Basic Concepts and Algorithms

Textbook: Tan, Pang-Ning. Introduction to Data Mining

1. What are the techniques for handling categorical attributes?
2. How do continuous attributes differ from categorical attributes?
3. What is a concept hierarchy?
4. Note the major patterns of data and how they work.

Q3. Read Ch. 7 in the textbook. Association Analysis: Advanced Concepts 

Textbook: Tan, Pang-Ning. Introduction to Data Mining

1. What is K-means from a basic standpoint?
2. What are the various types of clusters and why is the distinction important?
3. What are the strengths and weaknesses of K-means?
4. What is a cluster evaluation?

Select at least two types of cluster evaluations, and discuss the concepts of each method.

Answer all three questions in 250 words each. Follow APA 7 Guidelines. There must be APA formatted references (and APA in-text citation) to support the thoughts in the post.