Data mining

Topics: Data mining Pages: 95 (1825 words) Published: January 12, 2014
Assignment Cover Sheet
Faculty of Science, Engineering and Built Environment
NAME: Sagar Agarwal
STUDENT ID: 213188362
UNIT CODE: SIT717
ASSIGNMENT/PRAC No.: 2
ASSIGNMENT/PRAC NAME: Assignment 2
DUE DATE: 6th Oct, 2013

Plagiarism and collusion
Plagiarism occurs when a student passes off as the student’s own work, or copies without acknowledgment as to its authorship, the work of any other person. Collusion occurs when a student obtains the agreement of another person for a fraudulent purpose with the intent of obtaining an advantage in submitting an assignment or other work Declaration

I certify that the attached work is entirely my own (or where submitted to meet the requirements of an approved group assignment is the work of the group), except where work quoted or paraphrased is acknowledged in the text. I also certify that it has not been submitted for assessment in any other unit or course. I agree that Deakin University may make and retain copies of this work for the purposes of marking and review, and may submit this work to an external plagiarism-detection service who may retain a copy for future plagiarism detection but will not release it or use it for any other purpose. DATE: 06/10/2013

Introduction:
In data mining it is said that “success or failure often depends not only on how well you are able to collect data but also on how well you are able to convert them into knowledge that will help you better manage your business (Wilson, 2001, p. 26).” Tourism and hospitality industry generates massive amount of data. In each and every transaction there is set of data generated. In tourism and hospitality, knowing your customer is very important. Suppose there is a customer who likes adventure and the manager tries to sell him SPA service, then it is likely that the customer would refuse it. Knowing the customer and offering him the right service is very important. This increases the profit of the organization, also it helps to retain the customers and gain their loyalty. Thus knowing the customer in terms of their preferences and spending is very important. Data mining is the efficient way to know your customers. As the set of data is so huge, there is a need to use data mining tools to reveal facts and figures about the customers. Data mining not only helps to increase profit, but it also guarantees the survival of the organization in long run and gives a competitive edge. Unfortunately, however, the hospitality industry and managers are behind of using such data mining strategies, compared to the retail and grocery industries (Bogardus, 2001; Dev & lsen, 2000). Thus there is a need to spread awareness about such tools and techniques to hospitality industry. This paper shows some techniques and discusses the effective use of data mining in hospitality industry.

Overview:
We have analyzed online hotel website data for finding online purchasers. Here we have used IBM SPSS statistics tool and Weka [version 3.6.10) for analyzing the data Tools & Methodology
Tools :
1. Weka 3.6.10
2. IBM SPSS version 20
Methods
3. Association Rules
4. Contrast Set mining

Task 1. Association rules- Association Rules Is there any pattern in the data set for different groups of visitors? Solution-

Best rules found after performing analysis in weka tool

1. d2=High res3=High 203 ==> reservat=Yes 172 conf:(0.85) 2. res2=High res3=High star4=High 200 ==> reservat=Yes 169 conf:(0.85) 3. res2=High res3=High star4=High star5=High 199 ==> reservat=Yes 168 conf:(0.84) 4. res3=High star4=High 214 ==> reservat=Yes 180 conf:(0.84) 5. res3=High star4=High star5=High 213 ==> reservat=Yes 179 conf:(0.84) 6. res2=High res3=High star5=High 206 ==> reservat=Yes 173 conf:(0.84) 7. res3=High star5=High 223 ==> reservat=Yes 187 conf:(0.84) 8. d2=High star4=High 203 ==> reservat=Yes 170 conf:(0.84) 9. d2=High star4=High star5=High 202 ==> reservat=Yes 169 conf:(0.84) 10. res1=High...
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