Running head: DATA MINING

Ervin Franklin

December 7, 2014

Data Mining

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DATA MINING

Predictive analysis provides numerous benefits in businesses. It allows the business

personnel to rank customers in the market share and is a single value measured for each

customer. In addition, it is a reliable campaign in terms of response predictor when handling

customers who are ranked high by the business personnel. This constitutes a key form of

business intelligence. Through this model, the businessman extracts financial information from

the customer such as credit transactions which can enable him to determine if the customer can

be liable for future business transaction ("Data mining and predictive analytics in long-term

care," 2012). The predictive analysis will outlay prospects of retaining customers with similar

transaction behavior. For instance a customer who spends less in online business may be less

likely to renew an annual subscription to the same online business service. Predictive analysis

will also help the business person to conduct smarter rankings through combined prediction. The

right combination allows the business to perform better since the business personnel consider

multiple aspects of the customer and their behaviors.

Data Mining has some benefits as it relates to the association discovery model. It helps to

discover an interesting relationship between products in large scale transaction data records. It

helps the business person to research and identify the relationship between data that is seemingly

unrelated. For instance if a person buys eggs, he or she is more likely going to buy milk as well.

The main rule behind this particular aspect is the paradox relationship between products, which

helps the consumer determine his decision with an antecedent ‘if’ and the consequence ‘then’.

Such decisions can be used in promotional pricing or product placement. This aspect of data

mining is most applicable in predicting trends in reference to customer behavior (Singh &

Swaroop, 2013).

Web mining is another technique used by business persons to sort through various web

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resources to gather useful information which allows a company to help understand market

dynamics. The collected information instills more knowledge to the businessman hence enables

them to carry out predictions regarding customer preference. This aspect not only serves the

interests of consumers but also predicting the right organizational department’s decisions. The

information collected helps to develop more advanced protective measures to be undertaken to

prevent fraudulent business transactions ("Web data mining," n.d.).

Clustering in data mining is the task of discovering groups and structures in the data that

are somehow similar. In addition, it is applied in segmenting customer and market share as well

as the pattern and categorizing hierarchical density based business models. After understanding

the business categories the businessman is able to summarize and make positive assumption of

the market segment. Another benefit is that one is able to eliminate outliers in the collected data

(Rocco, 2011, pp. 3-4).

The reliability of data mining is an important requirement in deriving and extracting

meaningful information. The success of data mining depends on certain skills and knowledge

and ingenuity of the business researcher. The data collected by a business entity enhances

services rendered, as well as product life cycle. Improving the products lifecycle the businesses

are confident for high reliability (Rocco, 2011, pp. 3-4). There are five major elements for a

reliable data mining process. These elements include:

1. Extracting, transforming and loading transaction data onto a data warehouse system.

2. Storing and managing the data in the database system.

3. Provide the data access to business analysts and IT professionals

4. Analyze the data with the appropriate application software

5. Present the data in a useful format

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In addition to these elements, there are basic modeling principles that control how business

researchers use data mining (Rocco, 2011, pp. 3-4). These models provide purpose and helps in

predicting consumer behavior. However, there are problems associated with data mining. Data

mining can be trusted since it has been used with an intention to uncover hidden patterns

(Amooee, Minaei-Bidgoli & Bagheri-Dehnavi, 2011, pp. 425-431). For many years’ businesses,

scientist engineers and governments have used data mining to sift through large volumes of data

in order to predict upcoming trends. The data mining algorithm allows the researcher to make

comparison between variables in a phenomenon (Rocco, 2011, pp. 3-4). The main problem is

determining a mathematical model for an unknown input or output of data. Typically the

algorithms are aimed to reduce failures and minimize consequences associated with such. The

failures are noted when there is an interruption or the system stops functioning. Fault detection is

determined through two approaches; the model based approach and the data based approach.

These techniques are popular in solving such faults and failures within the system (Amooee,

Minaei-Bidgoli & Bagheri-Dehnavi, 2011, pp. 425-431).

Data mining process raises concerns among consumer privacy. The privacy infringement is

associated with liberty, autonomy solitude and secrecy (Tavani, 1999). The main concern raised

by most consumers is information privacy. This concern intrudes and interferes with personal

information that is regarded as confidential. This results to breach of psychological privacy or

associative privacy. This violation is contributed by:

1. The technology collecting information about consumers without their knowledge.

2. Consumers have the knowledge that their private information is being used by

business researchers but they have no say on how the information is being disclosed.

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3. There are no laws protecting consumers from the breach of confidential data

especially in the World Wide Web (Tavani, 1999).

All the concerns raised are valid. Data mining programs and technology are designed to

reveal information that is extremely difficult for data users to obtain without personal consent.

The consumers transacting their e-business are required to enter information that they have no

idea how the business person will use them. Crucial information that consumers enter when

logged in to online shops risks the bank accounts. Most consumers have complained that the

privacy act in the international law of trade have not covered privacy information for consumers.

For instance US privacy Act of 1974 and data protection guidelines have not covered privacy

information collected from consumers (Tavani, 1999). The privacy and security of user

information is a delicate issue and business persons are taking into consideration the sensitivity

of the matter. Business persons have vowed to keep the confidential information a secret in order

to win the confidence of the consumers (Dileep & Swaroop, 2013).

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References

Amooee, G., Minaei-Bidgoli, B., & Bagheri-Dehnavi, M. (2011, November 1). A comparison

between data mining prediction algorithms for fault detection (case study: Ahanpishegan

co.). International Journal of Computer Science Issues, 8(6, number 3). Retrieved August

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between-data-mining-prediction-algorithms-fault-detection-case-study-ahanpishegan-co

Data is a game changer. (n.d.). Retrieved August 24, 2013, from www.ibm.com:

http://www.ibm.com/smarterplanet/us/en/smarter-enterprise/case-studies/us-open.html?

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dc_36309380497_432t5q28552

Data mining and predictive analytics in long-term care. (2012). Retrieved from

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http://www.fuzionanalytics.com/FuzionAnalytics/media/Fuzion_Library/documents/12-

1964_Fuzion_Whitepaper.pdf

Demery, P. (2013, June 10). Wal-Mart buys a 'predictive analytics' firm. Retrieved August 24,

2013, from www.internetretailer.com: http://www.internetretailer.com/2013/06/10/wal-

mart-buys-predictive-analytics-firm

Rocco, C. M. (2011, January 1). Guest editorial: Data mining in reliability and risk assessment.

International Journal of Performability Engineering, 7(1). Retrieved August 24, 2013,

from http://www.ijpe-online.com/guest-editorial-data-mining-in-reliability-and-risk-

assessment.html#axzz2dg8lsTwC

Schultz, B. (2011, November 3). How predictive analytics turns banks into fortune tellers.

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Retrieved August 24, 2013, from spotfire.tibco.com: http://spotfire.tibco.com/blog/?

p=8642

Singh, D. K., & Swaroop, V. (2013). Data security and privacy in data mining: Research issues

& preparation. International Journal of Computer Trends and Technology, 4(2).

Retrieved August 24, 2013, from http://www.ijcttjournal.org/volume-4/issue-2/IJCTT-

V4I2P129.pdf

Tavani, H. T. (1999). Informational privacy, data mining, and the internet. Ethnics and

Information Technology, 1, 137-145. Retrieved August 24, 2013, from

http://link.springer.com/article/10.1023%2FA%3A1010063528863#page-1

Web data mining. (n.d.). Retrieved August 24, 2013, from www.web-datamining.net:

http://www.web-datamining.net/

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