Data mining has emerged as an important tool for identifying useful information from data in almost all industries. Industries are using data mining to increase revenue and reduce costs. According to Seidman (2001), “Data mining is particularly valuable for organizations that collect large quantities of historical information. Banks, insurance companies, credit card companies...use this technology to derive critical information from large, unwieldy data samples.” (p. 6).
“Web (data) mining refers to the whole of data mining and related techniques that are used to automatically discover and extract information from web documents and services. When used in a business context and applied to some type of personal data, it helps companies to build detailed customer profiles, and gain marketing intelligence.”(Wel 2004). One of the best known applications of data mining is in the financial sector; the use of individual credit risk assessments, done by banks, when determining if an applicant is a good “risk”. When applicants fill out loan applications, they are often asked to provide their social security number, address, and other identifying information. In addition, they are also required to give other pieces of information that say something about them. They are asked questions about how long they have been at an employer; whether the applicant is a home owner or a renter, how long have they lived at a current address, their marital status, educational level, etc. When banks require all this information, they can analyze the data that they get and discover correlations between applicants’ personal characteristics and the probability of a loan default. This way of using data mining to evaluate and examine all the variables that affect this outcome enables financial institutions such as banks to process hundreds of thousands of loan applications at one time, lowering operation costs through the use of fewer employees.
Another way data mining is used is in healthcare. According to Ramagerri (2013), “data mining can help healthcare insurers to detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services.” (p. 2). A hospital is a very complicated organization, where medical staff gives very efficient and specialized service for patients. Due to rapid advances in medical technology, organizations like hospitals are not robust enough to adapt to rapid changes. According to Karagupta, “Such rapid changes lead to malpractice of medical staff, sometimes a largescale accident may occur by chain reaction of smallscale accidents.” (p. 443). Data mining is used in the hospital as risk mining.
Risk mining is where data including risk information is analyzed, using data mining methods and the results are used for risk prevention. Risk mining consists of three major processes: risk detection, risk clarification, and risk utilization. The first process is risk detection by using acquired knowledge, patterns or other types of information can be identified that is unexpected.
The second process is risk clarification which means if domain experts need more information with finer granularity they collect more data with detailed information and apply data mining to newly collected data. The last process is risk utilization this is where experts evaluate clarified risk information in a realworld environment to prevent more risk events. If they do not have enough risk information for the prevention, then more analysis is required to finish the process.
In the airline industry, data mining is used to help the Customer Relationship
Management (CRM) processes. Most airlines utilize frequent flyer programs. A frequent flyer program proffers an abundance of data to the airline, allowing a better understanding of customer
types
PART 1 - DATA, TEXT, AND WEB MINING TECHNICAL REPORT 1 INTRODUCTION The purpose of this report is to examine the different types of mining and provide insight into their applicable use and relevance. It also provides a number of notable uses for each type of mining along with some personal reflection from the author on the implications of mining on privacy and ethics. 2 BACKGROUND Turban, Sharda and Delen (2011) define data mining as “a term used to describe discovering or mining knowledge from…
Abstract- Outlier detection is an active area for research in data set mining community. Finding outliers from a collection of patterns is a very well-known problem in data mining. Outlier Detection as a branch of data mining has many applications in data stream analysis and requires more attention. An outlier is a pattern which is dissimilar with respect to the rest of the patterns in the data set. Detecting outliers and analyzing large data sets can lead to discovery of unexpected knowledge in area…
What is data mining? How can it be integrated into the business strategies? Data is collected from different operational databases, this data is limited in scope and therefore is used mainly for transaction processing When the summarized data from different departments within the company is collected it is stored in a data warehouse in a multidimensional database. The information that can be pulled from this data is used to support decision making. However the way the information is stored in…
Abstract Simply collecting data for research is nearly a faux pas in today’s competitive web-market. Analysts are now looking toward the predictive analytics of association discovery in web and data mining, to find Business Intelligence of clustering sub=populations while eliminating errors to keep collected data valid. In the midst this data crunch are fears of lost privacy. Do not fear. Creative innovations are bringing mash-ups to our diversity. Data Analytics Report Useful information…
Harrah’s script Good evening everyone. I am Shixin. Today my group member Na and I will talk about a data mining case study about Harrah’s. In this presentation, we will introduce the company background at first, and then tell you the company’s objective. After discussing the current resource and application, we will come up with the method improvement. In the end, we will make a summary. Firstly, let’s look at the company background. Harrah’s is the world’s largest casino company. It is an entertainment…
Assignment 4: Data Mining Data mining is consider analyzed data-collect from different aspects and summary that is transformed into useful information. Companies does this Predicive analytics is a predictor which is measured per customer. For instance, recency, which prefers to the past customer to recent customers.there will be a higher value from past customers then present. "That means that if you contact your customers in order of recency -- first, call the most-recent customer; next, call…
Data Mining. •Data mining has been defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases" •Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. •Data mining tools predict future trends…