Chapter 31

Question 1

What are supervised mining techniques

Answer 1

The inputs are given by end user/user

Question 2

What are unsupervised mining techniques

Answer 2

The inputs are not given by end user/user. We do not how much classes and properties there and we are not guiding how to do data mining.

Question 3

What is similarity / dissimilarity ratio

Answer 3

The match/mismatch ratio of the matrix sets the target evolutionary distance

Question 4

What is time complexity of similarity matrix

Answer 4

n(square) x m

Question 5

What are main types of data mining

Answer 5

1- Supervised

2- Unsupervised

Question 6

What are types of supervised data mining

Answer 6

• Bayesian modeling
• Decision Tree
• Neural network etc

Question 7

What are 2 types of unsupervised data mining

Answer 7

1- One-way clustering

2- Two-way clustering

Question 8

What is one-way clustering

Answer 8

When we cluster a data matrix, we use all attributes and do rows clustering. It gives global view of data matrix.

Question 9

What is two-way clustering

Answer 9

We use columns and rows clustering in two-way in data matrix. It gives local view of data matrix

Question 10

What is min-max “distances” in clustering

Answer 10

Records are grouped with similarity constraint. In clustering, the intra-distance should be maximum e.g. clustering of employees in company with similar salary. Young people cluster is far away with old people cluster.

Question 11

How to identify association in records

Answer 11

Map the association in distance matrix. So we can quantify records with more similarity.

Question 12

What is numeric and non-numeric attributes

Answer 12

Numeric attributes are with numeric values and non-numeric attributes are with non-numeric values.

Question 13

Can graph be stored in matrix form

Answer 13

Yes. Matrix is a data structure that can store a graph.

Question 14

What is binary matrix

Answer 14

The matrix that has values 0 and 1

Question 15

What are 2 methods to find clusters in matrix

Answer 15

1. Graph portioning (Separate vertices which have more connectivity and less connectivity)
2. Click detection

Question 16

What is classification

Answer 16

Classification is a data mining function that assigns items in a collection to target categories or classes.

Question 17

How classification works

Answer 17

We take data set and convert it into 2 sets.
1. Training set
2. Test set
Training set is testify on test set and get 2 classes of it. So we can classify data.

Question 18

Clustering vs cluster detection

Answer 18

First do clustering and then do cluster detection. (note: once we have clusters then we can know how much number of clusters exists in system)

Question 19

What is K means cluster detection technique

Answer 19

K means clustering techniques use a mean point to categorize values in clusters. It is fast technique.

Question 20

What is mean point in clustering

Answer 20

The point in a cluster which defines in which cluster the value falls

Question 21

Does k means clustering supervised

Answer 21

Yes

Question 22

Does k means clustering converse

Answer 22

Yes