Data Mining
Determine the benefits of data mining to the businesses when employing:

Predictive analytics to understand the behaviour of customers

"The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved in the predictive analysis, these are: spotting the business problem, exploring various data sources, extracting patterns from data, building a sample model by making use of the data and problem, Clarify data -- finding valuable factors -- generating new variables, constructing a predictive model by making use of sampling and validating and deploying the model.

Decisions can be made very quickly by the business if they make use of this method as, they will have a lot of data to help…...

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References

Two Crows Corporation (1999) Introduction to Data Mining and Knowledge Discovery,  http://www.twocrows.com/intro-dm.pdf 

Angoss (2012) Predictive Analytics in the Cloud Solutions,  http://www.angoss.com/predictive-analytics-solutions/cloud-solutions 

Oracle.com (2008) Oracle Data Mining Concepts,  http://docs.oracle.com/cd/B28359_01/datamine.111/b28129/clustering.htm 

Tiwari, S. (2011) A Web Usage Mining Framework for Business Intelligence,  http://www.ijecct.org/v1n1/4.pdf

Data Mining in Health Care
Data mining has been used both intensively and extensively in many organizations.in the healthcare industry data mining is increasingly becoming popular if not essential. Data mining applications are beneficial to all parties that are involved in the healthcare industry including care providers, HealthCare organizations, patients, insurers and researchers (Kirby, Flick,.&Kerstingt, 2010).

Benefits of using data mining in health care

Care providers can make use of data analysis in identifying effective treatments and the best practices. This can be achieved through making comparison of causes, symptoms and adverse effects. Data mining can also be used in making analysis of the cause of action that will be effective for a specific group of patients. This can also be used in the identification of best clinical practices hence help in the development of guidelines and standards of care. To patients data mining is useful in that they can receive better and…...

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References

Kusserow R,(2010).Benefits of Data mining in HealthCare: The future has arrived. Retrieved 20 January from http://www.compliance.com/articles/benefits-of-data-mining-in-healthcare/

Kirby, M,.Flick, E.&Kerstingt, M.(2010).The Benefits of Digging Deeper: Using Data mining to improve Employee Health and reduce Employee costs. Retrieved 20 January from  http://www.sibson.com/publications/perspectives/volume_18_issue_2/digging-deeper.html 

Conjecture corporation.(2003). What Are the Different Types of Data Warehousing Tools? Retrieved 20 January from  http://www.wisegeek.org/what-are-the-different-types-of-data-warehousing-tools.htm

Data Mining
Businesses can receive many benefits from data mining. Which benefits they receive, however, can also depend on the way in which their data mining is undertaken. Predictive analytics are used to understand customer behavior, and businesses use the behavior of the customer in the past to attempt to determine what the customer will do in the future (Cabena, et al., 1997). While it is not an exact science, many companies believe they can use it in order to decide which products will sell most often to which customers (Nisbet, Elder, & Miner, 2009). Association discovery is another type of data mining, and is more involved with the products that are sold and how they match up to specific types of customers, as opposed to specific customers by name or other determination (Nisbet, Elder, & Miner, 2009). In other words, predictive analytics look at what customer A will buy again,…...

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References

Cabena, P., Hadjnian, P., Stadler, R., Verhees, J., & Zanasi, A. (1997). Discovering data mining: From concept to implementation. New York, NY: Prentice Hall.

Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.

Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis & data mining applications. New York, NY: Academic Press/Elsevier.

Data mining, a process that involves the extraction of predictive information which is hidden from very large databases (Vijayarani & Nithya,2011;Nirkhi,2010) is a very powerful and yet new technology having a great potential in helping companies to focus on the most important data in their data warehouses. The use of data mining techniques allows for the prediction of trends as well as behaviors thereby allowing various businesses to make proactive and yet highly informed knowledge-driven decisions. Data mining can therefore help businesses in answering various business questions that in the past have been considered too time consuming to analyze and solve. Companies can therefore collect as well as refine large quantities of data in order to gain a competitive advantage from the hidden predictive patterns contained within. In this paper we determine benefits of data mining to the businesses when employing:
Predictive analytics to understand the behavior of customers

Associations discovery in…...

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References

Ahmed, S., A. (2004) 'Applications of Data Mining in Retail Business', IEEE Computer society international Conference on Information Technology: Coding and Computing (ITCC'04).

Berry, M.J.A. And Linoff, G. (1997) Data mining techniques for marketing, sales and customer support, USA: John Wiley and Sons.

Chen, S., Y. And Liu, X. (2005) 'Data mining from 1994 to 2004: an application -- oriented review', International journal of business intelligence and data mining, vol.1, No.1, pp.4-21

Hui, S., C. And Jha, G. (2001) 'Application of data mining techniques for improving customer services', International Journal of computer applications in Technology, Vol.14, No.1-3, pp.64-77.

Data Warehousing and Data Mining
Executive Overview

Analytics, Business Intelligence (BI) and the exponential increase of insight and decision making accuracy and quality in many enterprises today can be directly attributed to the successful implementation of Enterprise Data Warehouse (EDW) and data mining systems. The examples of how Continental Airlines (Watson, Wixom, Hoffer, 2006) and Toyota (Dyer, Nobeoka, 2000) continue to use advanced EDW and data mining systems and processes to streamline their business models are a case in point. The greater the level of economic uncertainty, perceived and actual risk in any given strategy or endeavor, the more the reliance on EDW, data mining and advanced forms of predictive modeling including analytics (Sen, amamurthy, Sinha, 2012). From this standpoint, the emerging areas of high growth in the global economy are attracting a high level of investment in EDW, data mining, predictive modeling and analytics. The latest figures illustrate how valued EDW…...

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References

Applications to "visualize" the data stream. (1999). American Bankers Association.ABA Banking Journal, 91(3), 56-56.

Brachman, R.J., Khabaza, T., Kloesgen, W., Piatetsky-Shapiro, G., & Simoudis, E. (1996). Mining business databases. Association for Computing Machinery.Communications of the ACM, 39(11), 42-48.

Jeffrey H. Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.

Fay, C.P., & Zahay, D. (2003). Understanding why marketing does not use the corporate data warehouse for CRM applications. Journal of Database Marketing & Customer Strategy Management, 10(4), 315-326.

The use of databases as the system of record is a common step across all data mining definitions and is critically important in creating a standardized set of query commands and data models for use. To the extent a system of record in a data mining application is stable and scalable is the extent to which a data mining application will be able to deliver the critical relationship data, predictive analytics and accurately reflect the associations most critical to companies (Kuhn, Ducasse, Girba, 2007). The uses of multidimensional database systems are essential for creating the system of record on which data mining applications are based on. Data warehouses are the system of record these data mining applications rely on for completing more extensive analysis of the data sets they have available. The third process is the development of user-based applications that make queries of the data sets possible, including…...

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References

Berry (2004) - Survey of Text Mining Clustering, Classification, and Retrieval Berry, Michael W. (Ed.) 2004, XVII, 244 p. 57 illus., Hardcover ISBN: 0-387-95563-1

Buddhakulsomsiri, J., & Zakarian, a.. (2009). Sequential pattern mining algorithm for automotive warranty data. Computers & Industrial Engineering, 57(1), 137.

Cressionnie, L.. (2008). Ready for Takeoff. Quality Progress, 41(7), 59-61.

da Cunha, C., Agard, B., & Kusiak, a.. (2010). Selection of modules for mass customisation. International Journal of Production Research, 48(5), 1439.

The ability to parse through the many records of transactions, customer
contacts, and many other items stored electronically creates the foundation
for data mining's definition. Data mining specifically is defined as the
process of data selection, exploration and building models using vast data
stores to uncover previously unknown patterns, insights, and observations
that lead to strategies for effective differentiation and growth.
Central to the development of data modeling is the creation of data and
prediction models based on data collected from a variety of sources,
including corporate transactions, customer histories, and demographics,
even external sources such as credit bureaus and services organizations
that sell content (Westphal, C., Blaxton, T., 34). Companies
accomplishing best practices in data mining then use the many data and
prediction models to produce patterns in the information that can support
decision making and predict new business opportunities. What's unique
about data mining is the ability to quickly create entire snapshots and
background statistical and content-specific data quickly using seemingly
disparate and unrelated…...

Data Mining
The amount of knowledge available in today's world is massive. The information technology specialist who's responsible to his or her organization for maximizing the capacity for practical usage of this knowledge, it is becoming increasingly difficult to have a total grasp of the problem. The purpose of this essay is to discuss the importance of implementing data warehousing and mining systems inside an organization. In order to do this, it is necessary to contrast the positive benefits of data mining and contrast those ideas with the negative connotations associated with the similar processes.

Data mining, according to Thearling (2009) is "the automated extraction of hidden predictive information from large databases. " Additionally, data mining is a proactive and aggressive tactic that can serve the overall business strategy when properly aligned. Statistical analysis is inherent within any type of data mining technique and is expressed in these terms. The reasons why…...

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Bibliography

Betancourt, L. How Companies are using your Social Media Data,   business-intelligence-and-performance-management/http://www.enterpriseirregulars.com/5706/the-top-10-trends-for-2010-in-analytics -

Greenfield, L. (2005) The Case for Data Warehousing  http://www.dwinfocenter.org/casefor.html 

Greenfield, L.Greenfield, L. (2005) The Case Against Data Warehousing  http://www.dwinfocenter.org/against.html 

Thearling, K. (2009) An Introduction to Data Mining  http://www.thearling.com/dmintro/dmintro_2.htm

The tools used, in this case, for knowledge discovery and data mining where based on artificial neural networks (ANN) and consisted of four different models. All models represented supervised learning models with a known output. The four models of the ANN were dynamic network, prune network, the multilayer perceptron, and the radial basis function network.

The main challenge for its implementation was that data needed to be cleaned so the data mining process can easily retrieve these data and process them according to the required use. All abnormalities needed to be eliminated since the outcome would have been impacted by invalid or erroneous information.

The result of the business intelligence implementation was successful and the VHA could reach their goal of reducing the length of stay for each patient where necessary. Also a better productivity and a more efficient care program for their patients was a result of this implementation.

As this case…...

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Bibliography

Kraft, M.R., Desouza, K.C., & Androwich, I. (2003). Case Study of a Veterans' Administration Spinal Cord Injury Population. Retrieved April 24th, 2010, from Data Mining in Healthcare Information Systems: http://csdl2.computer.org/comp/proceedings/hicss/2003/1874/06/187460159a.pdf

Smalltree, H. (2006, July 20th). Business Analytics/Business Intelligence News. Retrieved April 23rd, 2010, from Business intelligence case study: Hospital BI helps healthcare:  http://searchbusinessanalytics.techtarget.com/news/1507291/Business-intelligence-case-study-Hospital-BI-helps-healthcare

Similarly, the Air Force needed no only some intelligent reporting capailities, ut a way that Air Force personnel, government employees, and civilian IT contractors would work together in the evaluation of applications and reports in a more roust and real-time manner. "The intent was to provide the Keystone user community the aility to do more complex financial analysis and reporting on a "self-service" asis to reduce overall system maintenance and development costs' (Air Force).

The SAP Business Oject tools are useful for management and organizations that already have a decent set of data, ut need access to it in ways that are more meaningful to the organization. For example, in the new social-media paradigm there are literally hundreds of different demographic and psychographic modifiers that might assist firms in moving from an older marketing paradigm to a new, service to the client model, all y using data mining and warehousing tools…...

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bibliography, but to keep track of methods, research items, and/or even models within reference works. Small business owners can find ways to shave time and money from processes; and using a data mining concept, they can control their own costs and inventory management, as well as touch the customer more often in a way that is more conducive to long-term relationship building.

REFERENCES

Bardoliwalla, N. (December 1, 2009). The Top 10 Trends for 2010 in Analytics, Business Intelligence, and Performance Management. Enterprise Irregulars. Retrieved from:  

Data mining is very important for operational effectiveness but when / how to stop mining data before it becomes more trouble than it's worth?
Over the last several years, advancements in technology have meant that an increasing number of companies are using data mining to be able to understand the demographics of their customers. This is when they will look at large amounts of information to figure out specific buying habits and patterns. However, the rise in the large number of corporations that are using these practices has been facing increasing levels of scrutiny. This is because these techniques are being utilized as a way to: understand the overall demographics of customers and their shopping patterns. As a result, the use of this technique has been the focus as to how and when data mining should be conducted by organizations. To understand this, we will look at a number of different…...

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Bibliography

Health Information Privacy. (2011). HHS. Retrieved from:  http://www.hhs.gov/ocr/privacy/hipaa/understanding/consumers/index.html 

Supreme Court Takes Up Case on Data Mining. (2011). PBS. Retrieved from:  http://www.pbs.org/newshour/bb/law/jan-june11/scotus_04-26.html 

Graettinger, T. (2010). Digging up Dollars. T Dan. Retrieved from:  http://www.tdan.com/view-featured-columns/12370 '

Reidy, J. (2005). Hard Sell. Kansas City, KS: Andrews MccMeel

Computer Science
The overall theme or focus:

The media industry is an industry that is resistant to the validity of data mining and the kind of insight data mining in this field could yield.

There are two primary pieces of software with respect to film and television editing. They are Avid and Final Cut Pro, while there are more programs available. The latest version of Final Cut Pro, FCPX, takes more a data mining perspective and approach to nonlinear editing. There were significant changes with respect to aesthetics, interface, and organization. There was a bit of a well documented uproar by various editors and other industry professionals with respect to these changes. There is a great resistance to the use of FCPX because of the data mining approach relative to other upgrades. This is just one example of media's resistance to data mining.

The media industry is still a bit behind with respect…...

algoithms that can mine mounds of data that have been collected fom people and digital devices have led to the adoption of data mining by most businesses as a means of undestanding thei customes bette than befoe. Data mining takes place in etailing and sales, banking, education, manufactuing and poduction, health cae, insuance, boadcasting, maketing, custome sevices, and a numbe of othe aeas. The analytical infomation gatheed by data-mining applications has given some businesses a competitive advantage, an ability to make infomed decisions, and bette ways to pedict the behavio of customes.
Wite a fou to five (4-5) page pape in which you:

Detemine the benefits of data mining to the businesses when employing:

Pedictive analytics to undestand the behavio of customes Associations discovey in poducts sold to customes

The collected data can help the manage detemine which poducts best inteest the customes and which may inteest them in the nea and futhe…...

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references. Much of this may be private aside from which the individual may not

data mining?
The foundational elements of data mining are multidisciplinary in nature, encompassing analytics, computer science, database systems integration and management, statistics and artificial intelligence. Often these technologies are used to create a single system of record used for analysis and advanced queries by the enterprises who build them. Data mining is often included in business intelligence (BI) suites and the analytics layer of an enterprise-wide computing system, as each application needs to gain access to the metrics and key performance indicators (KPIs) (Peacock, 1998). The use of data mining has become more pervasive in marketing, sales and service as organizations strive to gain insights from the terabytes of data they have accumulated over years and in some cases decades of operation. Data mining can provide marketers with greater insights into the preferences, needs and wants of customers, in addition to potential new product or service ideas based on a…...

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References

Craft, S.H. (2001). An empirical investigation of international consumer market segmentation decisions. The George Washington University). ProQuest Dissertations and Theses,, 155-155

Ganeshasundaram, R., & Henley, N. (2006). The prevalence and usefulness of market research: An empirical investigation into 'background' versus 'decision' research. International Journal of Market Research, 48(5), 525-550.

Koh, H.C., & Chan Kin, L.G. (2002). Data mining and customer relationship marketing in the banking industry. Singapore Management Review, 24(2), 1-27.

Peacock, P.R. (1998). Data mining in marketing: Part 1. Marketing Management, 6(4), 8-18.

2001, p.160).
esults also indicated that patients between the ages of forty-six and sixty-four incurred significantly larger financial losses for the facility than the rest of the patients and Medicare and Medicaid nonexempt patients admitted via routine admission had the highest average loss for inpatient visits. Also, on Tuesdays, the average loss per patient was significantly greater for patients in the region. Only with data-mining could such an apparently insignificant finding as the predominance of Tuesday admittances come to light. The reason for this was not random: "Examination of Tuesday's admitting physicians" revealed that "several of the physicians were in the same medical specialty. This specialty cared for patients that typically required a high level of service intensity over a long period of time. The identification of a subset of patients with disproportionately high costs has prompted the institution to reevaluate its admission criteria to this unit" and also the…...

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References

Silver, Michael. Taiki Sakata; Hua-Ching Su; Charles Herman; Steven B. Dolins; & Michael J. O'Shea. (2001, Summer). "Case study: How to apply data mining techniques in a healthcare data warehouse." Journal of Healthcare Information Management. 15(2). Retrieved June 5, 2010 at  http://www.himss.org/content/files/jhim/15-2/him15208.pdf

1. The use of genetic engineering in creating designer babies
2. The ethics of animal testing in scientific research
3. The impact of social media on privacy and ethical boundaries
4. The ethical implications of artificial intelligence and automation in the workforce
5. The ethical considerations of data mining and surveillance in the digital age
6. The ethical responsibilities of pharmaceutical companies in pricing and distributing life-saving medications
7. The ethics of factory farming and its impact on animal welfare and the environment
8. The ethical dilemmas surrounding end-of-life care and physician-assisted suicide
9. The ethical implications of technology companies manipulating user data for profit
10. The ethical considerations....

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6. The effects of automation on job displacement and income inequality
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Topic Development in Criminal Investigations: Exploring Critical Concepts and Contemporary Issues

1. The Interplay of Forensics and Criminal Investigation

The evolution of forensic science and its impact on investigation techniques
The challenges and limitations of forensic evidence in criminal prosecutions
The impact of forensic technology on case resolution and the justice system

2. The Psychology of Criminal Behavior and Its Application to Investigations

The psychological profiles of different types of criminals
Investigative techniques for understanding and predicting criminal behavior
The use of psychological theories to develop investigative strategies

3. The Role of Interviewing and Interrogation in Criminal Investigations

Ethical and legal considerations in....

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4. The environmental implications of cloud computing and its potential to reduce carbon emissions
5. The future of cloud computing: emerging trends and technologies
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