Data Mining Applications and Ethical Considerations

This assignment delves into the world of data mining, examining its diverse applications across healthcare, finance, and more. It analyzes the benefits and challenges associated with this technology, particularly focusing on security risks and the importance of establishing ethical guidelines to protect human rights in the context of big data analytics.

Assignment on Data Mining and Visualization
2017

Contents
Task 1: Data mining in business................................................................................................2
Introduction:...........................................................................................................................2
Use of data mining in business:..............................................................................................2
Article:....................................................................................................................................2
Task 2: Title: Security, Privacy, and ethics................................................................................3
Introduction:...........................................................................................................................3
Business Requirement:...........................................................................................................3
Identification of security, privacy, and ethical implication in data mining:...........................3
Significance of implication in business sector:......................................................................4
Responses:..............................................................................................................................5
Importance of current and future trends to affect data mining and visualization:..................5
Conclusion:................................................................................................................................6
References:.................................................................................................................................7

Task 1: Data mining in business
Introduction:
The data mining is the method used for applying sorting on the large data set for identifying
the pattern and relationship between them to solve a related problem. The future trends of the
business can be predicted by using data mining tools. It is commonly applied on the large
scale data for creating information processing with the help of methods like data collection,
warehousing, extraction, analysing, and statistics. It is applied on the application related to
decision support system, machine learning, artificial intelligence, and business intelligence.
The data mining tasks are semi-automatic in nature which identifies the pattern by cluster
analysis, anomaly detection and dependencies. The data mining procedure depends on six
major tasks which are categorised as Anomaly detection, association of rule learning,
clustering of activities, classification of data, regression of the function, and summarization
of the data set. The result of the data mining helps in predicting the future behaviour of the
activities and the performance of the product.
Use of data mining in business:
The data mining is used for identifying the hidden patterns which helps in prediction to
analyse the impact on businesses. It helps in achieving the goal and objective of the
organization. It helps in predicting the future sales of the product and services of the
organization. “The data mining tools helps in developing predictive modelling” (Maheshwari,
2015). The graphical user interface is used for analysing the data. It is used for segmenting
the customers, analysis of the market, forecasting the sales of the organization, managing
relationship with the customers, management of risks, and fraud detection.
Article:
Title: From mining to big data: Inner Mongolia’s economic development (Source: Jie, 2017
http://www.chinadaily.com.cn/business/2017-08/08/content_30365352_2.htm).
Business Requirement:
The diversification should be built up for changing traditional Mongolian organizational
infrastructure to the infrastructure with the specification of big data and cloud computing.
“The Mongolia industries are working on developing strong electricity power supply” (Jie,
2017). The big data and cloud computing helps in developing application at lower price. It is

working on developing agro processing enterprise which helps in increasing the agricultural
products. Foreign trade and ecommerce is at boom for the inner Mogolia.
Summary:
The data mining activities help in getting value added output which helps in increasing the
sale of the products. It is working on developing largest data centre in North China by
minging data from the all the sectors such as steel, agro-based, power, railways, and etc. The
data mining methodologies helps in improving the economic development of the country by
building risks models and fraud detection systems. The safety and quality issues associated
with the enterprise can be resolved by using the concept of data mining. The operation of the
organization can be improved by managing the relationship with the supply chain
department. The country is working in the direction to sold the raw material at lowest price
which helps in increasing the sale of inputs and products. The inclusion of foreign trade and
technology of e-commerce brings a major change in the working tactics of Mongolian
industries.
Task 2: Title: Security, Privacy, and ethics
Introduction:
“Data mining is the information technology which is used for handling various databases and
large amount of data in the effective manner” (Apte, 2011). The data mining helps in
predicting th logical pattern of the data set. With the application of the data mining
methodology, there are some issues of security, privacy, and ethical implication associated
with it.
Business Requirement:
The privacy, security, and ethical issues is the major concern areas for managing the big data
on the distributed computing environment. “The big data infrastructure opens the path for the
potential attacks” (Petre, 2013). The data mining can act as a solution to the problems
encountered in managing the big data.
Identification of security, privacy, and ethical implication in data mining:
The table below helps in identifying the security, privacy, and ethical issues associated with
the data mining dilemma.
Issues Descriptions

Security Issues The big data security is required for
managing the sheer scale of the people. The
security professionals works on protecting
the big data sets. The problem of handling
big data is associated with the distributed
computing technology associated with the
large scale industries such as Amazon. The
working of the organization is divided into
global operational areas for creating data
centres which helps in protecting the
organization from physical and cyber-attacks.
Privacy Issues The management of privacy is the major
issue with big data management. The
physical spaces should be controlled from the
accessing of information. The big data
infrastructure opens the path for the potential
attacks. Underutilization of the resources and
data generally take place due to the privacy
issues associated with the flow of
information.
Ethical Issues Ethical standards should be followed for
pursuing public goods. The databases should
not be hacked. The human should use the
internet and associated information when he
has a right to access the internet. The
unauthorised user should not access the
information.
Significance of implication in business sector:
The tools of data mining help in handling the data in the secured manner. “The use of Hadoop
is the best alternative for managing the unauthorised accessing of data in the management of
big data on the platform of distributed computing” (Roddick, 2016). The accuracy and quality
of the information can be improved by managing the big data from potential attacks. The big

data analytics solution helps in picking and tracking the occurrence of malicious emails. The
early detection of fraud helps in providing promising results.
Responses:
“The management of big data by using data mining tools helps in improving the user
experience” (Paidi, 2012)). The tracking of big data is easier to analyse. For example,
Insurance Company has to manage big data profile by involving questioning coverage, the
problems can be solved by realistic options, and helps in fraud detection of the company.
“The data Anonymization is the process which can be used for overcoming the problems and
risks associated with the privacy issues” (Fule, 2015). The data can be accessed without
authorisation when:
 It is based on data driven research which provides value and benefits to the society.
 The queries should not be generated by the data user. The excessive risks can be
reduced
 The data and action should be full transparent to the data users.
 The discrimination activities should be punished.
 Compensation mechanism should be used for dealing with discrimination activities
 The potential gains should be estimated by the data users.
 The democracy can be achieved in decision making process by using the ethical
standards in the working curriculum of the organization.
Importance of current and future trends to affect data mining and visualization:
“The technology of data mining helps in dealing with large amount of data located at
different databases” (Tasioulas, 2016). The data mining technology helps in creating new
opportunities for automatic prediction of trends and related behaviour, automation in the
discovery of unknown patterns, predictive model for artificial neural network, development
of decision tree by using the technology of classification and regression tree and the Chi
square automatic interaction detection, genetic algorithms are the basic methodology used for
optimization techniques, k-nearest neighbour technique, and significant use of rule induction
methods.
Current Trends:

The methodology of data mining is used in creating process to fight against terrorism,
development of bio-information for creating cure methods for diseases, development of web
and semantic web by using the resource description framework, in the cluster analysis,
prediction techniques, in the development of business intelligence system, and others. It is
presently mainly applied in the field of healthcare sector, retail industry, finance,
telecommunication, web mining, text mining, and higher education.
Future trends:
The data mining technology is going to be used in the near future in the areas of ubiquitous
data mining, development of mining related with the hypertext and hypermedia resources,
multimedia channels related to data mining, and others. The focus is also given on the areas
related to geographic and spatial data mining, sequence and time series development, data
mining based on constraints, and phenomenal based data mining. The data mining concept is
used for developing social welfare program. “The human rights should be given privilege for
determining the concept of data mining in the field of big data analytics” (Tasioulas, 2016).
Conclusion:
The risks and gains are associated with the use of big data in the working culture of the
organization. The security is the major challenge which come forth in the effective utilization
of the data. The minimization of the risks takes place by fixing the human rights and ethical
standards for the changing condition and working requirements of the organization.

References:
Apte, C. (2011). The role of data mining in business optimization. Retrieved from
https://www.siam.org/meetings/sdm11/apte.pdf
Fule, P. (2015). Detecting privacy and ethical sensitivity in data mining results. Retrieved
from http://crpit.com/confpapers/CRPITV26Fule.pdf
Jie, Z. (2017). From mining to big data: Inner Mongolia’s economic development. Retrieved
from http://www.chinadaily.com.cn/business/2017-08/08/content_30365352_2.htm
Maheshwari, S. (2015). Data mining concepts, application, and research direction. Retrieved
from https://www.ijarcce.com/upload/2015/november-15/IJARCCE%2088.pdf
Paidi, A. (2012). Data mining future trends and applications. Retrieved from
http://www.ijmer.com/papers/Vol2_Issue6/ES2646574663.pdf
Petre, R. (2013). Data mining solution to the business environment. Retrieved from
http://www.dbjournal.ro/archive/14/14_3.pdf
Roddick, J. (2016). On the ethical and legal implications of data mining. Retrieved from
https://csem.flinders.edu.au/research/techreps/SIE06001.pdf
Ryoo, J. (2016). Big data security problems threaten consumer privacy. Retrieved from
http://theconversation.com/big-data-security-problems-threaten-consumers-privacy-
54798
Tasioulas, J. (2016). Big data, human rights, and the ethics of scientific research. Retrieved
from http://www.abc.net.au/religion/articles/2016/11/30/4584324.htm