Data Mining - Lecture Introduction

Question 1

What is data mining?

Answer 1

The creative process that provides results useful for decision making.

This process can include statistics, machine learning and programming among others, and is often used in big data.

Question 2

What are the 5 V’s of big data?

Answer 2

1. Volume
2. Velocity
3. Variety
4. Veracity
5. Value

Question 3

What is meant by volume in big data?

Answer 3

The amount of data

Question 4

What is meant by velocity in big data?

Answer 4

The speed at which data is being generated and changed.

Question 5

What is meant by variety in big data?

Answer 5

The different types of data being generated (text, dates, numbers etc.)

Question 6

What is meant by veracity in big data?

Answer 6

The accuracy or truthfullness of a dataset

Question 7

What is meant by value in big data?

Answer 7

Data only has value is it is turned into something useful

Question 8

What is an algorithm?

Answer 8

A specific procedure used to implement a specific data mining technique.

Question 9

What is nominal data?

Answer 9

Data that serve as labels (often textual). There is no ordering in the values and there is no ranking.

Question 10

What is ordinal data?

Answer 10

Nominal data with an order between the values.

hot > mild > cool

Question 11

What is interval data?

Answer 11

Data with numerical values where there is an order and there are set and specific intervals between the values. There is no defined zero point.

Question 12

What is ratio data?

Answer 12

Data with numerical values, where there is an order and there are set and specific intervals between the values. There is also a defined zero point.

Question 13

What are the 10 steps in the data mining process?

Answer 13

1. Develop an understanding of the purpose of the project.
2. Obtain the dataset to be used in analysis
3. Explore, clean and preprocess the data
4. Reduce the data dimension, if necessary.
5. Determine the data mining task
6. Partition the data (for supervised tasks)
7. Choose the data mining technique
8. Use algorithms to perform the task
9. Interpret the results of the algorithms
10. Deploy the model (run it on real records)

Question 14

What is CRISP-DM?

Answer 14

Cross Industry Standard Process for Data Mining.
Also gives steps.

1. Business understanding
2. Data understanding
3. Data preparation
4. Model Building
5. Testing and Evaluating
6. Deployment

Question 15

What is SEMMA?

Answer 15

Also gives steps for the process.

1. Sample
2. Explore
3. Modify
4. Model
5. Assess

Is a cycle. Similar to CRISP’s step 4-5-6

Question 16

Student number?

Answer 16

2064381