University Data Mining and Visualization Report - Semester 2, 2024

This report provides a comprehensive overview of data mining, exploring its applications in business and the associated ethical and security concerns. Task 1 focuses on the importance of data mining, its practical uses in various business contexts like market segmentation, customer churn prediction, fraud detection, and direct marketing, and highlights its benefits across finance, marketing, and retail sectors. It includes a discussion of a recent article showcasing how data mining is revolutionizing the tourism industry. Task 2 delves into the critical analysis of security, privacy, and ethical issues in data mining, detailing major security threats, privacy concerns, and ethical implications like stereotyping and data misuse. The report emphasizes the importance of addressing these issues to ensure data integrity, user trust, and responsible business practices. It discusses the need for robust database security, client authentication, and adherence to ethical guidelines to prevent data breaches and maintain customer privacy. The report concludes by underscoring the necessity for businesses to understand and mitigate these risks to leverage data mining effectively.

Running head: DATA MINING AND VISUALISATION
Data Mining and Visualisation
Assignment Number
Student ID
Name of the Student
Name of the University
Author’s note

1DATA MINING AND VISUALISATION
Executive Summary
The improvement of data mining is exhibiting critical moral and social issues that must be
tended to in the event that the new innovation is to broadly acknowledged. This paper
investigates a scope of these issues recognizing specifically: protection, information
exactness, database security, stereotyping, lawful risk and the more extensive research issues.
Each issue is talked about and the suggestions for strategy improvement are investigated.
This report incorporates some thought of conceivable arrangements furthermore, proposes
roads for encourage examination. This report also highlights an article where data mining
technique is used to facilitate tourism industry in Australia.

2DATA MINING AND VISUALISATION
Table of Contents
Task 1.........................................................................................................................................3
1. Why Data mining is used in business....................................................................................3
1.a. State the importance of data mining................................................................................3
1.b. How businesses could use data mining...........................................................................3
1.c. Discuss the benefits of using data mining.......................................................................4
2. Recent article related to data mining in business- “Google Australia tells Tourism Australia
data mining is the industry’s future”..........................................................................................5
Introduction............................................................................................................................5
Body.......................................................................................................................................5
Conclusion..............................................................................................................................6
References..................................................................................................................................6
Task 2.........................................................................................................................................7
Analysis of Security, Privacy and Ethical issues and implications in data mining....................7
1.0. Introduction.....................................................................................................................7
2.1. Major security issues in data mining...............................................................................7
2.2. Privacy issues in data mining..........................................................................................9
2.3. Ethical implications in data mining.................................................................................9
2.4. Importance of these implications in data mining..........................................................10
3.0. Conclusion.....................................................................................................................10
4.0. References.........................................................................................................................11

3DATA MINING AND VISUALISATION
Task 1
1. Why Data mining is used in business
1.a. State the importance of data mining
With the huge measure of information put away in records, databases, and different
storehouses, it is progressively critical, if a bit much, to grow effective means for
investigation and maybe elucidation of such information and for the extraction of fascinating
learning that could help in basic leadership (Adam & Wortmann, 2012).
1.b. How businesses could use data mining
For organizations, data mining is utilized to find examples and connections in the
information with a specific end goal to enable settle on to better business choices (Agrawal &
Srikant, 2013). Information mining can help spot deals patterns, create more quick witted
showcasing efforts, and precisely foresee client dedication. Particular employments of data
mining include:
• Market division - Identify the regular attributes of clients who purchase similar items
from your organization.
• Customer agitate - Predict which clients are probably going to leave your organization
and go to a contender.
• Fraud location - Identify which exchanges are destined to be deceitful (Agrawal &
Srikant, 2013).
• Direct promoting - Identify which prospects ought to be incorporated into a mailing
rundown to acquire the most astounding reaction rate.
• Interactive advertising - Predict what every individual getting to a Web webpage is in
all probability keen on observing.

4DATA MINING AND VISUALISATION
1.c. Discuss the benefits of using data mining
There are many advantages of data mining. For instance:
• In fund and managing an account, data mining is utilized to make exact hazard
models for credits and home loans. They are likewise exceptionally accommodating when
identifying false exchanges.
• In promoting, data mining methods are utilized to enhance changes, increment
consumer loyalty and made focused on publicizing efforts (Fule & Roddick, 2014).
• Retail stores utilize client shopping propensities/points of interest to upgrade the
design of their stores keeping in mind the end goal to enhance client experience and
increment benefits.
• Tax overseeing bodies utilize data mining methods to distinguish false exchanges and
single out suspicious assessment forms or different business records.
• In fabricating, information disclosure is utilized to enhance item wellbeing, ease of
use and solace.
2. Recent article related to data mining in business- “Google Australia tells
Tourism Australia data mining is the industry’s future”
Introduction
This is a subsequent answer to Tourism Research Australia's (TRA) depiction on the
effect of the mining blast, discharged in November 2011. It demonstrated that travel action to
the key "mining" tourism locales (and for business all the more for the most part) had
expanded firmly and that convenience and flying enterprises in capital urban communities
and in the mining ranges were profiting from the blast up to that time (Fule & Roddick,
2014).

5DATA MINING AND VISUALISATION
Body
This investigation takes a gander at these financial effects in more profundity at a
state and region level, including for recreation tourism (go for the reasons for occasion, or to
visit companions and relatives – VFR). As tourism is a troublesome industry in which to
decide the net effects of the mining blast, a state-based Computable General Equilibrium
(CGE) demonstrate was utilized to help evaluate this effect (Piatetsky-Shapiro, 2016).
Endeavoring to measure the effects of the mining blast is made more troublesome because of
the local idea of effects that presently accessible insights can't reflect.
To give an industry point of view on the insights and displaying, TRA looked for
view from delegates from various tourism associations at present dynamic in the mining blast
face off regarding, including the Tourism and Transport Forum Australia (TTF), the
Australian Tourism Export Council (ATEC), Queensland Tourism Industry Council (QTIC)
and the Tourism Council Western Australia (TCWA).
Conclusion
The business criticism affirms the more extensive patterns contained in official
insights, in particular, that while the mining blast effect on the tourism business has been
extremely blended, it has negatively affected recreation tourism. The criticism likewise
featured worries about tourism's failure to draw in and hold talented staff and the effect that
the dislodging of relaxation goes with business travel (mineworkers) is having on the
recreation tourism segment. This report recognizes in detail a scope of financial effects of the
mining blast, both positive and negative.

6DATA MINING AND VISUALISATION
References
Adam, N. R. & Wortmann, J. C. (2012), ‘Securitycontrol methods for statistical databases: A
comparative study’, ACM Computing Surveys 21(4), 515–556.
Agrawal, R. & Srikant, R. (2013), Privacy-preserving data mining, in W. Chen, J. Naughton
& P. A. Bernstein, eds, ‘ACM SIGMOD Conference on the Management of Data’,
ACM, Dallas, TX, pp. 439– 450.
Fule, P. & Roddick, J. F. (2014), Detecting privacy and ethical sensitivity in data mining
results, in V. Estivill-Castro, ed., ‘27th Australasian Computer Science Conference
(ACSC2004)’, Vol. 27 of CRPIT, ACS, Dunedin, New Zealand, pp. 159–166.
Piatetsky-Shapiro, G. (2016). Advances in knowledge discovery and data mining (Vol. 21).
U. M. Fayyad, P. Smyth, & R. Uthurusamy (Eds.). Menlo Park: AAAI press.

7DATA MINING AND VISUALISATION
Task 2
Analysis of Security, Privacy and Ethical issues and implications in data
mining
1.0. Introduction
The data mining procedure is exhibiting noteworthy security, protection and moral
issues that must be seen and in view of that the organizations must suggest legitimate
techniques to counter those issues.
This report will grandstand every one of the issues identified with security and
protection and furthermore specifies the procedure by means of which the issues can be
fathomed.
2.1. Major security issues in data mining
Databases are imperative and fundamental segments of various government and
private associations. To ensure the information of the databases utilized as a part of
information distribution center and after that data mining is focal topic of security framework
(Schreuders & van Kralingen, 2013). The necessities of data mining security worried about
the accompanying characteristics.
Physical Database Integrity: When control comes up short the middle of the road
records is not posted or recovered accurately. Because of this the data mining winds up
noticeably unfit to foresee design by given applications (Big data security problems threaten
consumers' privacy, 2017).
Consistent Database Integrity: Whenever this happens the data mining calculation
can't have the capacity to foresee remedy data because of intelligent respectability
peculiarities with given database for data mining.

8DATA MINING AND VISUALISATION
Component Integrity: The honesty of every individual component is fundamental for
the database which is utilized for the data mining, if every component of database of
information distribution center keeps up the uprightness, there is no way for change by
human oversight and by whatever other projects (Big data security problems threaten
consumers' privacy, 2017).
Auditability: The date, time, fields, records and the past estimation of the records
ought to must be recorded under a log document (Big Data, Human Rights and the Ethics of
Scientific, 2017). This guarantees the best possible change is gone up against the database
executed under the information distribution center.
Get to Control: Database framework has the capacity for the get to control. This get
to control guarantees the get to benefits of information things from the database. This implies
who can read, adjust, erase the records or individual fields of the database. This get to control
is characterized by the database head for the clients of the undertaking (Roddick & Lees,
2013). In the event that a client has just benefit to peruse the information things of database,
at that point he or she can just observe the records however can't do anything others. The
database director can have a wide range of benefits on the database.
Client Authentication: Database administration framework requires the regrows
client validation. Without legitimate client ID number and secret word, the database does not
enable the client to do anything on information things of database (Big Data, Human Rights
and the Ethics of Scientific, 2017). Every client has its own client confirmation and
distinguishing proof substance. The client needs to keep its client ID and watchword mystery.
2.2. Privacy issues in data mining
This is required for every person who works the data mining instruments. Protection
is worried about individual client. The individual obligations are to keep the information

9DATA MINING AND VISUALISATION
things undisclosed to others. The organization ought to need to instruct the representatives
about the protection and its related angle time to time as indicated by assaults and ruptures of
current situations and past situations (Roddick & Lees, 2013). Information protection inside
kept up with the assistance of various sorts of trustworthiness requirements.
2.3. Ethical implications in data mining
The organizations confront a moral problem whether they should take clients' opinion
or not while they endeavor to store the individual data of the clients in their database. The
organizations if take conclusions from the clients to store information of the clients, the
clients may waver, subsequently this delay may hurt its aggressive edge in the market (Big
data security problems threaten consumers' privacy, 2017). The organizations who are putting
away the clients' information must act capably, something else, the, if the information gets
hacked or abused, the organization, will lose the clients' trust and can even cause harm. In
this manner, the organizations who need to utilize the information mining strategies to
improve the business exercises must act dependably, they should know about all the moral
issues related with it thus they ought not abuse the information (Roddick & Lees, 2013).
The information mining can isolate individuals on the premise of sexual, racial and
religious settings. This kind of routine with regards to information mining is both
untrustworthy and illicit Therefore, the clients must know about the actualities on how their
own information ought to be put away in the database; so the clients can know about the
results they can confront in future (Clifton et al., 2012). Along these lines, if the clients' data
is gotten to in future, the clients can know where their information is being utilized.
In this manner the moral worries in information mining can be seen as two essential
moral settings and can be identified with independence and isolation. The significance of
independence and isolation must be mulled over and must be esteemed to ensure each client

10DATA MINING AND VISUALISATION
gets treated sensibly (Denning, 2012). The organizations must know about the moral
concerns identified with information mining, in this way, they should actualize the
information mining system in an effective approach to ensure their clients' close to home
information not being traded off.
2.4. Importance of these implications in data mining
In the retail segment information mining helps in distinguishing client shopping
conduct, client maintenance strategies, dissect client gatherings and associate every last
gathering to the advantageous gathering (Kantarcioglu, Jiashun & Clifton, 2014). The
retailers must know about the security, protection and moral issues if the client data gets
bargained; it will influence their business and will likewise influence organization's notoriety
(Miller& Seberry, 2015). The retailers ought to be cautious when managing the bank cards
while managing the cash exchange that is the reason they execute exceptional security and
protection arrangements in their particular premises.
3.0. Conclusion
It can be concluded from the above talk that the moral, security and protection issues
have been examined intricately in this report and the likely arrangements have additionally
been talked about. This article additionally exhibits the utilizations of information mining in
business. Information mining is utilized astutely in retailing, keeping money, human services
and about each industry. That is the reason it is the obligation of the individual businesses to
adapt up to the security and protection issues and should execute an appropriate information
mining procedure to ensure they can be fruitful in their wander not bargaining the clients'
close to home information.

11DATA MINING AND VISUALISATION
4.0. References
Big data security problems threaten consumers' privacy. (2017). The Conversation. Retrieved
5 August 2017, from http://theconversation.com/big-data-security-problems-threaten-
consumers-privacy-54798
Big Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC
Religion & Ethics (Australian Broadcasting Corporation). (2017).
Abc.net.au. Retrieved 5 August 2017, from
http://www.abc.net.au/religion/articles/2016/11/30/4584324.htm
Roddick, J. F. & Lees, B. G. (2013), Paradigms for spatial and spatio-temporal data mining,
in H. Miller & J. Han, eds, ‘Geographic Data Mining and Knowledge Discovery’,
Research Monographs in Geographic Information Systems, Taylor and Francis,
London, pp. 33–49.
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X. & Zhu, M. (2012), ‘Tools for privacy
preserving data mining’, SigKDD Explorations 4(2), 28–34.
Denning, D. (2012), ‘Secure statistical databases with random sample queries’, ACM Trans.
Database Systems 5(3), 291–315.
Kantarcioglu, M., Jiashun, J. & Clifton, C. (2014), When do data mining results violate
privacy?, in W. Kim, R. Kohavi, J. Gehrke & W. DuMouchel, eds, ‘10th ACM
SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD ’04’, ACM
Press, Seattle, WA, pp. 599–604.
Miller, M. & Seberry, J. (2015), ‘Relative compromise of statistical databases’, Australian
Computer Journal 21(2), 56–61.

12DATA MINING AND VISUALISATION
Schreuders, E. & van Kralingen, R. (2013), Klantenkaarten, chipcards en data-mining; een
(juridische) verkenning, in R. v. Kralingen, M. Lips & C. Prins, eds, ‘De kaarten op
tafel; Een verkenning van de juridische en bestuurskundige aspecten van chipcards (in
Dutch)’, SDU Uitgevers, Den Haag, pp. 99–115.