Data Mining Essay

Data is defined as useful raw material which is intended to be useful for both the originator and for the intended receiver. Data consists largely of facts and figures ideal for communicating the intended meaning. This data can be interpreted and can be categorised as follows;

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

Data is the form of input, which can be produced into information. For example, 5000 could be the value of a piece of data that a business has. By adding context to this, for example if an organisation sold 1000 sandwiches in a week; this becomes information, which will influence important decisions

Data mining is essentially the ability to discover new information by exploring through various databases of existing information. According to Laura and Jack Cook, data mining "facilitates the discovery of previously unknown relationships among the data. …These operations present results that users already intuitively knew existed in the database."[2] As an example, let us take a school system consisting of three databases: one which stores the student profiles consisting of name and identification number, another to store student grades based on identification number, and the last one stores all the transactions at the bookstore through the student identification card. This is a simple example, but it should illustrate our point. Alone, the separate databases might not tell us much. With data mining techniques, the process might be able to tell us that in a particular school year, students of a certain ethnic background obtained above a 3.0 GPA, or that the bookstore sold mostly engineering books to students last year, or even that students who obtained above a 3.0 GPA were ones who bought engineering books. More specifically, the technology might be smart enough to associate that John Doe from Ireland had a 3.32 GPA in his engineering classes, even though he did not buy any engineering books from the bookstore. This type of technology is very powerful source of

Data analysis is a procedure of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. There are multiple facts and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains in data analysis. For data analysis we have to mine the data first for our purpose such that the data we can handle easily. Basically for data analysis our first thing to do our planning, how we are going to collect the data, our going data going to make sense or not, actually data will be meaningful for our object, after

Data comprises of factual information. Data are the facts from which information is derived. Data is not necessarily informative on its own but needs to be structured, interpreted, analysed and contextualised. Once data undergoes this process, it transforms in to information. Information should be accessible and understood by the reader without needing to be interpreted or manipulated in any way.

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

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 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.

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,

Data mining is another feature that is used in spreadsheets to manipulate complex data. Data mining is a feature that allows user to extract specific features such as filtering the names of students by surname. An example is if Ridgeway College needed to find contact details of students enrolled in 2016 all they would have to do is to filter the years and type in 2016, this will

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

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.