Application exploration: Traditional data mining applications had a great deal of attention on helping business gain well than others of a comparable nature. Data mining is explored to an increasing extent in areas such as financial analysis, telecommunications, biomedicines, science and also for counterterrorism and mobile (wireless) data mining. Scalable and interactive data mining methods: Data mining must be able to handle large amount of data efficiently and interactively apart from the existing data analysis methods. Constraint based data mining helps user to guide data mining systems in their search for interesting pattern. Integration of data mining with database systems, data warehouse and Web database systems: Data mining must ensure to serve as an essential data analysis component that can easily integrate with information processing environment such as database systems, data warehouse systems and web database systems. Standardization of data mining language: Standardization will pay way for the systematic development of data mining solutions; improve interoperability ...
Traditional business intelligence tools are being replaced by data discovery software. The data discovery software has numerous capabilities that are dominating purchase requirements for larger distribution. A challenge remaining is the ability to meet the dual demands of enterprise IT and business users.
To make the best of the seemingly untappable resource, a new field of data extraction, visualization, management and manipulation has come about – Data Analytics or Data Science. People who indulge in this data mining
McGonigle and Mastrian (2013) defines data mining as a process of utilizing software to sort through data so as to discover patterns and ascertain or establish relationships. They also state this process may help to discover or uncover previously unidentified relationships among the data in a database. Data mining is very important to healthcare organizations. It can help in ways such as to determine treatment effectiveness, identify problems, decrease costs for the organization, and can even detect possible fraudulent activity. Not only is data mining used in healthcare, but it is also used in other businesses as well. Although data mining is a great asset to healthcare, an informatics nurse has to be very careful due to the lack of a standardized
One of the biggest dilemmas in owning a store and providing a service in selling a product is figuring out how much of the item to get, if an owner gets too much of a product, the owner will lose money, if the owner does not get enough of a product, customers will shop somewhere else and the owner will not only lose the sale, but also potentially lose a customer who is willing to shop somewhere else to get the product they want. This past Christmas, I was shopping for the latest Frozen Movie Soundtrack for a gift. I had to go to three stores to find the CD to purchase. If the original stores that I went to had the product in stock, I most likely would have bought several more items.
Data mining is the technique to interpret the data from other perspective and summarize the data so that the data can be useful information. Technically, data mining is a process to identify relations or patterns in the databases to predict the likelihood of future events. According to Eliason et al, there are three systems for healthcare organization to implement the mining data systems. The three systems are the analytics system, the content system and the deployment system. The analytics system is a system that used to collect all data such as patients clinical data, patients financial data, patients satisfactory data and other data. The content system is used to store all medical evidenced data. The deployment system is used to make new organization structure. There are several elements that consist in data mining which are first extract, transform and load transaction data onto the data warehouse system, second, store and manage the data in a multidimensional system, third, provide data access to information technology professionals, forth, analyze the data by application software and lastly, present the data in graph or table format.
Joseph P. Bigus. 1996. Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support. McGraw-Hill, Inc., Hightstown, NJ, USA.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Normalization is the process of identifying the one best place where each fact belongs, it is being used to minimizinge data redundancy and optimizinge data structure by systematically and properly placing data elements in appropriate g...
Information is being shared online each time users browse the internet. This information is being disclosed from visited sites, and is being used by companies to better target customers. Sites, such as Facebook, Twitter, and Google plus are capable of tracking browsing activities, not only within the site, but beyond those boundaries. From each Facebook page like, to Twitter’s tweet button, personal information is made available to others. This paper will explore the technical and ethical considerations of the personal information being shared on the web for targeting customers based on their likes. This paper also covers the issues and concerns of data mining, and the privacy of online consumers. I will explain how personal information is obtained, and what purposes it is used for. I will also cover ways in which we can control how our information is being handled, and how to keep information out of other people’s hands.
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
use the data from the Data Base to help me prove. However I will not
It has reached the day and age where accurate and real time prediction tools are needed in modern clinics and hospitals. To utilize predictive medicine it is important to use the right trends of data mining methodologies to get accurate results (Paramasivam et al. 2014).
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.