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access to phone calls, emails, web searches, bank accounts, financial activity, medical records and school records (Garson, 2006; Berson, Smith, & Thearling, 2000). This paper presents an analysis of data mining practices that are used by the government in order to secure or uncover people’s data.
Data mining has many advantages in different fields. As massive data based on the information of thousands of individuals, is analyzed, this gives useful knowledge that can help in many ways. Data mining helps in saving time of the experts to make assessments on their own. Today, tools like the Microsoft SQL Server, Rapid Miner, Rapid Analytics and many other programs has made data mining accessible to everyday business users (Koutonin & Remarque, 2014).
The most important use of data mining is in the marketing and retail business. Data of consumers is analyzed to build models which predict the trend of choice of the consumers in the market. This trend can also help in launching a new product according to the demands and likes of the buyers. Data mining is a useful tool in giving financial information to banks about the illegal activities performed by some clients. It helps in tracking the fraudulent card transactions to protect the clients. Data mining has been employed in health care centers to analyze different aspects of health of the people. It helps in deducing results according to the demographics and its use has shown improvement in the medical care of the patients. Operational engineering data can detect fault equipment with the help of data mining and determine parameters that can help in controlling such activity (Rajaraman & Ullman, 2011).
Data mining tools are interactive, understandable and work directly on the data to present results. But, there are many disadvantages that have surfaced with the use of this technology tool. The most widely spread criticism is the breach of privacy of individuals. Tools of data mining are not only used by the
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According to the research findings, it can, therefore, be said that common health problem encountered by many people in the world can be avoided through ensuring that people get appropriate information about their causes, symptoms, and remedies. Certainly, Thyroid disorder is one of the health problems that could be avoided.
Therefore it is scientific that a true data mining software application or technique must be able to change data presentation criterion and also discover the previously unknown relationships amongst the data types. Data mining tools allow for possible prediction of the future trends and behaviors, hence enabling for formation of proactive, knowledge-driven decisions.
This is realized through predictive analysis data mining, which offers the users, impactful insights throughout the organization (Greene, 2012). Predictive analytics is where statistics and mathematics integrate to business and marketing to establish patterns in data and extrapolating the patterns to future business cases and issues, so as to reduce costs, improve response rates, increase the efficiency of processes and consequently boost revenue levels.
The major organizational element, in this case, is the customers. The predictive scores inform the business about the most probable action by the customer. The production of predictive scores occurs when the subject organization design a predictive model.
One such area where data plays a key role is auditing. Auditing is a crucial role carried out by all companies as a test of their own procedures and products. To assist auditors, companies deploy massive databases to capture all relevant data from all departments; this can be used by auditors to assess the company's internal control.
The first is Discovery, or the practice of examining data without a pre-determined hypothesis, in order to discover patterns in it. The discovery stage may occur by classification of data on the basis of clusters, association rules among sets of data, sequential
In discovery driven method, the data is scoured to identify patterns and hidden information. Future values of unknown variables are ‘guessed’ using the predictive modelling technique. However, the forensic method focuses on extracting unusual elements
The heart of customer relationship management (CRM) and personalized marketing programs is data-mining. Spikes can understand the customer’s behavior and preferences by using CRM technologies such as database
Examples of data mining software are oracle, Microsoft SQL Server 2012 and SAS.
KXEN provide an automated data mining for high productivity model building. It focuses on expanding the use of data mining within analysts, making them more
Healthcare practitioners, home-based caretakers and the elderly need to develop positive attitude towards the issue hence the increasing scholarly works on the subject. Critical evaluation on the article Fall Prevention Knowledge, Attitude, and Practices of Community
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