Data Mining in a Nut Shell In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer.
Data Mining is generally used for four main tasks: (1) to improve the process of making new customers and retaining customers; (2) to reduce fraud; (3) to identify internal wastefulness and deal with that wastefulness in operations, and (4) to chart unexplored areas of the internet
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Populations of data are resented by chromosomes and then go through a process of evolution. The members of one set of data compete to pass on their most favorable characteristics to the next generation of data. This process continues until the best data is found. Many of the models and algorithms used in data mining are simplifications of the linear regression model.
Data Mining is largely, if not entirely used for business purposes. The highest users of data mining include banking, financial, and telecommunications industries (Two Crows). A survey taken by Two Crows Corporation turned up these applications of data mining:
· Ad revenue forecasting
· Churn (turnover) management
· Claims processing
· Credit risk analysis
· Cross-marketing
· Customer profiling
· Customer retention
· Electronic commerce
· Exception reports
· Food-service menu analysis
· Fraud detection
· Government policy setting
· Hiring profiles
· Market basket analysis
· Medical management
· Member enrollment
· New product development
· Pharmaceutical research
· Process control
· Quality control
· Shelf management/store management
· Student recruiting and retention
· Targeted marketing
· Warranty analysis
Data mining will have a different effect on different industries in the business world. In the telecommunications industry, for example, in order to retain
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 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 is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major stepsexploration, model building and validation and finally, deployment.
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.
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,
In today’s society businesses accumulate all the data they can gather into the data warehouse, from which they can do data mining. This means that when we go to the grocery store and use our “saver card”, to get the tiny percent off, that store automatically tracks what you purchased and then enters it into their data warehouse. This allows the businesses to later go through that information and look for particular trends, seeing what products are popular what time of year, and as well as try figure what would we most likely to purchase. The art of looking through the data warehouse is referred to as data mining.
1) Data mining is a way for companies to develop business intelligence from their data to gain a better understanding of their customers and operations and to solve complex organizational problems.
As coined in an article in the St. Louis Post-Dispatch by Aisha Sultan, “Data is the new world currency.” Data mining is the process of analyzing data from different perspectives and then summarizing it into useful information. In essence is it applying all different types of what if scenarios on large swaths of data to get possible results to aid in better decision making. This sort of decision making isn’t something new, it’s the technology aiding the decision making that is new. This has reduced the amount of time it takes in the decision making process and given the
Data mining can aid direct marketers by providing them with useful and accurate trends about
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated work load. When medium to large-sized businesses use data mining, they uncovering these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.
Data mining is used in variety of fields and applications (Galit, Stiumueli, Natin & Peter 2010). This includes the military for purposes of intelligence,
From my understanding, data mining is a series of operation to dig up a value-added process from a bunch of data in the form of knowledge that is not known for manually. Knowledge discovery in database is a term that we called for data mining in science computer. Data mining also about to find a new information in a lot of data. Not only that, data mining is searching for patterns or relationships in one or more databases and it way to generate new information. Besides that, for secondary use, the information collected for one purposed used for another purpose and the information about customers is a valuable commodity. But, does we know how the data mining is work?
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
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.