### Case study on data mining | INTRODUCTION TO DATA MINING

**Abstract <>**

**Questions**

1. The following attributes are measured for members of a herd of Asian elephants: *weight, height, tusk length, trunk length,* and *ear area*. Based on these measurements, what sort of similarity measure from Section 2.4 (measure of similarity and dissimilarity) would you use to compare or group these elephants? Justify your answer and explain any special circumstances. (Chapter 2)

2. Consider the training examples shown in Table 3.5 (185 page) for a binary classification problem. (Chapter 3)

(a) Compute the Gini index for the overall collection of training examples.

(b) Compute the Gini index for the Customer ID attribute.

(c) Compute the Gini index for the Gender attribute.

(d) Compute the Gini index for the Car Type attribute using multiway split.

3. Consider the data set shown in Table 4.9 (348 page). (Chapter 4)

(a) Estimate the conditional probabilities for *P*(*A|*+), *P*(*B|*+), *P*(*C|*+), *P*(*A|-*), *P*(*B|-*), and *P*(*C|-*).

(b) Use the estimate of conditional probabilities given in the previous question to predict the class label for a test sample (*A* = 0*, B* = 1*, C* = 0) using the naıve Bayes approach.

(c) Estimate the conditional probabilities using the m-estimate approach, with *p* = 1*/*2 and *m* = 4.

**Conclusion<>**

**Grading Rubric for the Assignment #2:**

- Delivery: Delivered the assignments on time, and in correct format: 25 percent
- Completion: Providing a thoroughly develop the document including descriptions of all questions: 25 percent
- Understanding: Demonstrating a clear understanding of purpose and writing a central idea with mostly relevant facts, details, and/or explanation: 25 percent
- Organization: Paper is well organized based on the APA format, makes good use of transition statements, and in most instances follows a logical progression including good use of symbols, spacing in output: 25 percent