Data Mining

You are given data about seismic activity in Japan, and you want to predict a magnitude of the next earthquake, this is an example of

(i) supervised learning
(ii) unsupervised learning
(iii) serration
(iv) dimensionality reduction

Differentiate between supervised and unsupervised classification.

Bayesian classifiers is

(i) a class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory
(ii) any mechanism employed by a learning system to constrain the search space of a hypothesis
(iii) an approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation
(iv) None of the above

The decision boundaries produced in nearest-neighbor classifiers are

(i) rectilinear
(ii) oblique
(iii) arbitrary
(iv) None of the above

The margin of the decision boundary in SVM is given by

(i) $d = \frac{1}{|w|}$
(ii) $d = \frac{1}{2 \cdot |w|}$
(iii) $d = \frac{2}{\|w\|}$
(iv) $d = \frac{1}{|w|^2}$

The classification and prediction methods are affected by

(i) accuracy
(ii) speed
(iii) robustness
(iv) All of the above

Classification is a process of:

(i) Assigning data to predefined classes
(ii) Dividing data into clusters
(iii) Summarizing data
(iv) Cleaning data

Backpropagation algorithm is commonly used in:

(i) Decision Trees
(ii) Association Rules
(iii) Neural Networks
(iv) Hierarchical Clustering

Case-based learning is

(i) a class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory
(ii) any mechanism employed by a learning system to constrain the search space of a hypothesis
(iii) an approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation
(iv) None of the above

The training time is _______ and testing time is _______ of ANN.

(i) low, high
(ii) high, low
(iii) low, low
(iv) high, high

How does instance-based classifier work? List two instance-based classifiers.

What do you mean by under-fitting and over-fitting of a classification model?

Build a decision tree using the training data in the table given below. Divide the height attribute into ranges as follows :
\{0, 1.6\], (1.6, 1.7\], (1.7, 1.8\], (1.8, 1.9\], (1.9, 2.0\], (2.0, 5.0\]

Gender	Height (m)	Class
F	1.6	Short
M	2	Tall
F	1.9	Medium
F	1.88	Medium
F	1.7	Short
M	1.85	Medium
F	1.6	Short
M	1.7	Short
M	2.2	Tall
M	2.1	Tall
F	1.8	Medium
M	1.95	Medium
F	1.9	Medium
F	1.8	Medium
F	1.75	Medium

What is over-fitting and under-fitting of a model? When do they happen?

Why is naive Bayesian classification called 'naive'? Briefly outline the major ideas of naive Bayesian classification.

What is boosting? State why it may improve the accuracy of decision tree induction.

The results of an exam are recorded along with some data about the students. The results can be found in the table below :

ID	Cell No.	Language	Passed all Assignments	GPA	Passed Exam
1	93333-11101	Java	No	3.1	Yes
2	93333-11112	Java	No	2.0	No
3	93333-11102	C++	Yes	3.5	Yes
4	93333-11113	Python	Yes	2.5	Yes
5	93333-11103	Java	Yes	3.9	No
6	93333-11114	C++	No	2.9	No
7	93333-11104	Java	No	1.9	No
8	93333-11115	Python	Yes	3.2	Yes

In no more than one page of text, describe the design of a K-Nearest Neighbour classifier to predict if a student will fail or pass the exam.

What is data classification? How does it differ from prediction?

Define Classification and Prediction in data mining. Describe the concept of Bayesian Classification with a simple example.

Using the dataset from Q5a, use your K-NN classifier to predict whether the following student (who overslept and missed the original exam) will pass the re-exam :

ID	Cell No.	Language	Passed all Assignments	GPA	Passed Exam
9	93333-11109	C++	Yes	3.0	??

Describe the ID3 algorithm for decision tree construction. Why is it unsuitable for decision tree construction?

Explain the working of the Decision Tree method for classification.

Draw decision tree for the following data sets. Use entropy as a node selection mechanism:

Outlook	Temp (F)	Humidity	Windy	Class
Rainy	Hot	High	False	No
Rainy	Hot	High	True	No
Overcast	Hot	High	False	Yes
Sunny	Mild	High	False	Yes
Sunny	Cool	Normal	False	Yes
Sunny	Cool	Normal	True	No
Overcast	Cool	Normal	True	Yes
Rainy	Mild	High	False	No
Rainy	Cool	Normal	False	Yes
Sunny	Mild	Normal	False	Yes
Rainy	Mild	Normal	True	Yes
Overcast	Mild	High	True	Yes
Overcast	Hot	Normal	False	Yes
Sunny	Mild	High	True	No

The support vector machine (SVM) is a highly accurate classification method. However, SVM classifiers suffer from slow processing when training with a large set of data tuples. Discuss how to overcome this difficulty and develop a scalable SVM algorithm for efficient SVM classification in large data sets.

Discuss the working of Support Vector Machines and their applications in data mining.

Why do we use ensemble methods? Describe an ensemble method.

Explain Naive Bayesian classification algorithm.

Describe classification accuracy. How do we measure it? Differentiate classification accuracy with precision.

Define Classification and Prediction. List the major issues in Classification and Prediction.

Discuss overfitting and underfitting in decision tree construction with suitable example.