Lecture 1

Question 1

What is data mining?

Answer 1

identifying
novel
potentially useful
ultimately understandable patters in data. 
(Paitesky-Shapiro)

Question 2

What fields is data mining a combination of?

Answer 2

Machine learning
Application domain
Databases
Statistics
Visualization

Question 3

What is the difference between data mining and statistics?

Answer 3

datasets in data mining typically are:

• samples
• larger
• nosier, incomplete, heterogeneous

statistics

• often deals with the whole population
• often is concerned with hypothesis testing

Question 4

What is big data?

Answer 4

data that is too large to be analyzed with today’s resources

Question 5

What are some questions that we can answer with data mining techniques?

Answer 5

Is this object a star or galaxy?
Are customers likely to buy bread together with milk?
What is the value for a particular stock going to be in …
What book is a customer likely going to buy?
…

Question 6

What are the 7 main data mining tasks?

Answer 6

Sequential pattern discovery
Outlier detection
Association rule discovery
Regression
Clustering
Visualization
Classification

Question 7

Describe the data mining task “classification”

Answer 7

Assigning a category to each object in a data set

Question 8

Describe the data mining task “visualization”

Answer 8

Vizualizing the data. What does the data look like?

Question 9

Describe the data mining task “clustering”

Answer 9

Determining groups of objects in a data set.

Question 10

Describe the data mining task “Association Rule Discovery”

Answer 10

Determining which objects belong together in a data set.

Question 11

Describe the data mining task “Outlier Detection”

Answer 11

Determining which objects do not belong with the rest.

Question 12

Describe the data mining task “Sequential Pattern Discovery”

Answer 12

Determining what happens in the data over time.

Question 13

Describe the data mining task “Regression”

Answer 13

Assigning a numerical value to each object in the data

Question 14

Explain the data mining process

Answer 14

Collect data
Prepare data
Build model
Evaluate model
Deploy model

Question 15

How must data be prepared for neural networks?

Answer 15

wants numbers (categorical attributes must be transformed)
likes data to be scaled
does not like noisy data, especially for small datasets
can handle irrelevant or redundant attributes, while they may lead to large decision trees

Question 16

How must data be prepared for decision trees?

Answer 16

work better with discrete attributes that have small numbers of possible values
does not care about scaling

Question 17

Can nearest-neighbour data mining techniques handle noise well?

Answer 17

approaches can handle noise if a certain parameter is adjusted

Question 18

How must data be prepared for distance-based approaches

Answer 18

do not work well if the attributes are equally weighted and typically work with numerical data only

Question 19

Can expectation expectation maximization data mining techniques deal with missing data?

Answer 19

approaches can deal with missing data, but k-means techniques require substitution of missing data

Question 20

Describe why data transformations are used in data mining?

Answer 20

data must be transformed into an appropriate type for specific data mining techniques to work properly.

eg. neural networks only like data to be numerical values

Question 21

What are the two types of data mining techniques?

Answer 21

Predictive (supervised)

Descriptive (unsupervised)

Question 22

Describe predictive (supervised) data mining techniques

Answer 22

predict (discrete or continuous) class attributes based on other attribute values

this is like learning from a teacher

Question 23

Describe descriptive (unsupervised) data mining techniques

Answer 23

discover structure of data without prior knowledge of class labels.

Question 24

Is the following data mining technique descriptive or predictive?

Classification

Answer 24

Predictive

Question 25

Is the following data mining technique descriptive or predictive?

Visualization

Answer 25

Descriptive

Question 26

Is the following data mining technique descriptive or predictive?

Clustering

Answer 26

Descriptive

Question 27

Is the following data mining technique descriptive or predictive?

Association Rule Discovery

Answer 27

Descriptive

Question 28

Is the following data mining technique descriptive or predictive?

Outlier detection

Answer 28

Predictive or Descriptive

Question 29

Which data mining technique is the following:

is this object a star or a galaxy?

Answer 29

Classification

Question 30

Which data mining technique is the following:

What book is this customer likely to buy? Are there additional books we should recommend?

Answer 30

Clustering & Visualization

Question 31

Which data mining technique is the following:

How many groups of customers are there in the data we collected?

Answer 31

Clustering & Visualization

Question 32

Which data mining technique is the following:

Are customers likely to buy bread together with milk?

Answer 32

Association rule discovery

Question 33

Does traffic we currently see in our network contain any malicious packets?

Answer 33

Outlier detection

Question 34

What is a class label?

Answer 34

A label which identifies the class of the observation in a data set.

A class is just a way to identify a specific type of observation.

An example might be a data set containing both normal and abnormal readings from a sensor where each sensor reading is a combination of measurements (e.g. temperature, humidity, etc.).

The class label for a given sensor reading, in this case, would be normal or abnormal and the measurements would have to meet certain criteria to be identified as either class.

This would be a two class situation, you can have data sets which have multiple classes of observations or none.

Question 35

What is an application domain?

Answer 35

An application domain is simply the domain under study.
For example, you might be looking at data mining techniques for biological data, industry, or astronomy, to name a few. In these cases biology, industry, and astronomy in turn would be the application domains for each of the previously mentioned data sources.