1.Briefly summarise why data mining is used in Business. 

 

2.Share a recent article/news item relating to data mining in Business.

 

2.Discuss about the Security, Privacy and Ethics.

 

 

1.In present days, all the businesses flourishing in the market has to compete a lot to achieve a success. Every business has to accumulate large number of data in the form of employee’s data, reports of market analysis, information about the customer and data regarding sales (Fan & Bifet, 2013). More profitable execution is claimed by business using data mining. The data that is to be organized comes in various forms. To keep the data in an organized form, data mining is widely in use by the business or enterprises.

The process of data mining helps the corporation to condense the data into some valuable information and helps to coordinate huge amount of data (Hofmann & Klinkenberg, 2013). In data mining, data are cleaned to remove errors, data are integrated and also important data are selected for the data mining. Data mining is mainly very important part of a business that deals with large numbers of data.

Data mining is used in business in many fields. Temporary techniques that are used in a business are algorithms and techniques that contain mathematical equations (Larose, 2014). Some of the following aspects are stated below where business uses data mining are

• Decision trees- Data mining are used in business to make decision trees. Structures like tree shaped are used in business to organize the data.
• Induction rule- If then rules are extracted from data that are based on the statistical significance.
• Networks of Artificial neural- The non linear models which are learnt from training are the network of artificial neural.

Using data mining has many benefits-

Trade or Marketing- Business models that depends on the data that are accumulated in past that accepts campaigns that depends on the strategies of the modern market are the work that is done by data mining (Lin, Yao & Zadeh, 2013). Businesses that trade in the same manner also gets benefited by data mining.

Banking of businesses- The loans that are taken from bank are all recorded by data mining. The evidences are to be kept in an established way so that when they are needed it can be presented.

Manufacturing- The defects from the manufacturer are also recorded with data mining.

Governments- Government related issues such as paying taxes are kept in record by data mining.

2.Introduction

Logic-ITA, a web based information tool used in Sydney University from 2001 and it is considered as the second author of the University (Lara & Labrador, 2013). Over the last decade the tool is been used in the University. The main aim of this tool is to give the teacher information of the progression of the class and also helps the students to practice logical formal proofs.

Explanation

The tutorial data mining tool Logic-ITA is used over a decade in the Sydney University that helps the student of the University to practice their formal proofs of logic and gives the teacher detailed information about the progress of the class. This tool consists of two sets of formulas, one known as premises and the other known as conclusion. The main objective of the tool is that to derive conclusion from premises in a valid way (Mostafavi & Barnes, 2017). To perform this, students using the tool is build new methods and formulas step by step along with logic formulas and guidelines that were previously defined in continuing proof until completing the derivation. An error message is posted on the student’s wall if the methods or procedure performed by the student are incorrect (Witten et al., 2016). The database of all students studying in the University is recorded in the tool and is used by the teachers. The teacher contains two tables in which one is used for the mistakes done by the students and the other table is for all the correct steps done by the students.

Conclusion

The tool of Logic-ITA benefits the students to search the risk the students are facing and helps them who have not done the training properly. Visualization of data mining enables the teachers to find out the names of the students who have not failed their task properly.

2.Introduction

Every business has to accumulate large number of data in the form of employee’s data, reports of market analysis, information about the customer and data regarding sales (Tasioulas, 2017). The data requires more useful execution in an enterprise. The data or information that are present in an enterprise are present in many forms. Data mining is done to establish these information or data. Algorithms and techniques that are used in the business are all detailed using data mining. Data mining is a costly method, but the enterprises or businesses that deals with huge amount of data have to deal with those data using data mining (Ryoo, 2017). The best way to less the cost of maintenance is by outsource services. This outsource services of data mining attempts all types of mining the data such as data miming in stock market.

Analysis

Security Issues in Data Mining (article: ‘Big data security problems threaten consumers’ privacy’)

Issues that are related with security in data mining are as follows

• It is difficult to mine knowledge of distinct types.
• A unified learning of mining at different methods is done.
• Languages of data mining that are related with query and ad-hoc of data mining.
• Expressions that are related to data mining and visualization related data mining.
• Difficult to evaluate pattern of the correct and incorrect solutions.
• Scalability and efficiency of algorithms of data mining are done.
• Handling complex and relational types of data.
• Distributed, incremental and parallel methods of mining.
• Incomplete and handling noise.

Data Mining Privacy Issues

In the past decade years, data mining has faced huge compilations. A same kind of innovation to build the danger of the potential security is done by information mining (Neuman, 2016). Undertaken testing is to be done by data mining (Papamitsiou & Economides, 2014). The security of the people is maintained by organizing an information mining of an enterprise.

Ethical Implication of Data Mining

Moral ramifications for organizations utilizing information mining are not quite the same as lawful ramifications. Playing out a burglary is characterized as unlawful, yet notwithstanding considering endeavoring to endeavor a robbery is named deceptive. The worries among open is that when organizations even endeavor to utilize their shopping data or other information to target them back with more items, they think of it as untrustworthy. However, regardless of this, morals encompassing information mining is a hazy area (Wang, Kung & Byrd, 2016). The whole innovation can’t be viewed as great or terrible since it has numerous valuable points of interest for the general population great as well.

The applications of data mining are divided into many parts for benefits of the company and to look after the morals of the client for an effective business (Shmueli & Lichtendahl, 2017). The data of a company will never decrease from time to time. With the increment of the computer systems in a company, the number of information also increases.

The transparency of the data mining and to keep a look so that the data does not gets hacked is the main ethical concern of data mining (Roiger, 2017). The employees must be aggressive to implement the two aspects to help the customers to not lose their personal data and save them from being breached.

Human Rights on Big Data (article: ‘Big Data, Human Rights and the Ethics of Scientific Research’)

Grounding- For grounding of human rights, two ideas are accepted. Human rights are not major measure, but have their survival by which they can protect other data as well. The second is that only one value of master that includes freedom of human is not protected by the human rights. It supports many other values.

Content- The interests of the users are protected by the human rights. The extent up to which to impose a duty for protection is identified by human rights (Vicini et al., 2016). The work of duty bearer and restrict from doing something which is right is to be done. The practical content is the duty that is related with human rights.

Incompleteness- Moral standards of human rights are incomplete. All the proper standards that are useful to public policy and law are impoverished by human rights. The duties that are related to own self, solidarity and charity duties are not included in the human rights. The hacking of these kinds of duties is unethical.

Conclusion

When data mining becomes an important in a business development, no process or methods can be done without the data mining. The data or information of the customer or client is all protected in the information mining. It does not allow the breach of data in organization. The judgments weather to keep their personal information with the company is decided by the client only. To enroll management of data or information in an enterprise is all done by using data mining in modern business world. Simple numbers and data into categories are done by current data mining. More complex types of data may be included in the process of data mining in the future. By testing other relationships and variables, the models of data mining can be made better for more security of data protection and human rights against data protection. New methods and development are going on discovering and they are tested and implemented so that the models of data mining are improved and make the system more secure.

References

Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), 1-5.

Hofmann, M., & Klinkenberg, R. (Eds.). (2013). RapidMiner: Data mining use cases and business analytics applications. CRC Press.

Lara, O. D., & Labrador, M. A. (2013). A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials, 15(3), 1192-1209.

Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.

Lin, T. Y., Yao, Y. Y., & Zadeh, L. A. (Eds.). (2013). Data mining, rough sets and granular computing (Vol. 95). Physica.

Mostafavi, B., & Barnes, T. (2017). Evolution of an intelligent deductive logic tutor using data-driven elements. International Journal of Artificial Intelligence in Education, 27(1), 5-36.

Neuman, W. L. (2016). Understanding research. Pearson.             

Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of Educational Technology & Society, 17(4), 49.

Roiger, R. J. (2017). Data mining: a tutorial-based primer. CRC Press.

Ryoo, J (2017). Big data security problems threaten consumers’ privacy. The Conversation. Retrieved 12August2017, from https://theconversation.com/big-data-security-problems-threaten-consumers-privacy-54798

Shmueli, G., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

Tasioulas J. (2017).  Big Data, Human Rights and the Ethics of Scientific Research – Opinion – ABC Religion &   Ethics (Australian Broadcasting Corporation). Abc.net.au. Retrieved 12 August 2017, from https://www.abc.net.au/religion/articles/2016/11/30/4584324.htm

Vicini, P., Fields, O., Lai, E., Litwack, E. D., Martin, A. M., Morgan, T. M., … & Robson, M. (2016). Precision medicine in the age of big data: The present and future role of large?scale unbiased sequencing in drug discovery and development. Clinical Pharmacology & Therapeutics, 99(2), 198-207.

Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.