The Information Age: Data Mining Essay

Searching of data (Data Mining) when particular data is needed due to an urgent matter can be very hard for both employees and owner of the business firm;

To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the

On the morning of September 11, 2001, millions of Americans, and many more around the world, woke up to heart-wrenching news of a horrific magnitude. Two planes had collided into the twin towers of the World Trade Center in New York, a third rammed into the Pentagon in Washington, D.C., and yet a fourth crash-landed in Philadelphia. All victims of this carefully planned act of terrorism, several hundreds of innocent lives were lost in the direct assaults on the planes, a couple of thousand more were injured or killed from the damage caused by the attacks, and hundreds more were sacrificed in the rescue attempts.[1] It is unthinkable that such a large scale operation could have been

Data mining is defined as the process of data selection and studying and building models using massive data stores to disclose previously unidentified patterns in databases (Koh and Tan, 2005, p. 64). Koh and Tan have found financial institutions, marketers, manufactures and so has many other agencies have used data mining. Data mining has been of great use by various organizations. For example, data mining has been useful to detect fraudulent credit card transactions (Koh and Tan, 2005, p. 64). Koh and Tan stated, “In healthcare, data mining is becoming increasingly popular, if not increasingly essential” (Koh and Tan, 2005, p.64). In healthcare there have been reports that data mining has been successful in detecting fraud and abuse in healthcare claim (Koh and Tan, 2005, p.65). There are many factors in healthcare that have driven the use of data mining applications. One of the factors that have driven healthcare to use the data mining applications will be the medical insurance fraud and abuse. All organizations currently involved in the healthcare industry can profit from the data mining applications. For example, data mining is able to help

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

An important measure of any database management system is how effectively related data can be collected, organized and presented as reports, and also for business intelligence. What techniques from data warehousing and data mining would be used to facilitate this.

As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

1.Identify the potential sales and department store transactions that can be stored within the database.

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

Data mining allows companies to focus on the more important information in their data warehouses. Data mining can be broken down into two major categories. Automated prediction of trends and behaviors, and automated discovery of previously unknown patterns. In the first category, data mining automates the process of finding predictive information in large databases. Questions that traditionally required exhaustive hands-on analysis can now be quickly answered directly from data. In the second category, data mining tools sweep through databases and identify previously hidden patterns in one step. This category is where the major focus of research has been on.

Data preparation is one of the most significant and time-consuming phases of data mining projects (Steinbach et al., 2005; Han et al., 2006; Yau, 2011). The data needs to be prepared to be in an appropriate state for analysis, maintaining its representativeness of the real world but in a format that is appropriate for the data analysis tools. Therefore, data pre-processing techniques like data selection, data cleaning, constructing new data, integrating data and transformation of data were used in this case study.

Since higher education has blurred the lines with traditional businesses, it is important to have the tools to assist them with valuable data and information, in making decisions. Using of data and having the right data mining tools can insure the institute’s success, in many forms, such as, identifying market trends, precision marketing, new products, performance management, grants and funding management, student life cycle management and procurement to mention a few. To get a better grasp on these benefits it’s important to understand data warehouse, data mining and the associated benefits.