Data Mining

Data Mining

Introduction

In the process or practice of data mining, huge amount of data can be searched automatically. This process assists in determining the trends and patterns which appear to be complex and difficult to analyze. Data mining uses sophisticated algorithms for assessing the probability of forthcoming events as well as for data segmentation. These data mining algorithms are based on mathematics. Data mining has another name also which is known as Knowledge Discovery in Data (Oracle, 2005).

Data Mining Benefits

Data Mining and Predictive Analytics

In data mining, large amount of data is used for analyzing with the help of the use of predictive analytics. Extraction of correlations and patterns is the method which has been adopted in data mining. This method includes the application of mathematical and statistical models for analysis. The tools of analytics are emerging in business organizations with the passage of time and these tools work like a core drivers for business in order to provide competitive advantage to the business organizations (Fuzion Analytics, 2012).

Predictive analytics is relevant to the use of business intelligence data for modeling and forecasting. In predictive analytics, data is used for predicting future patterns. Predictive analytics are in use in the medical, credit and insurance industries. Assignment and evaluation of credit score is possibly the most extensively identified application of predictive analytics. By the use of past events, managers of the organization estimate the probability of upcoming events through predictive analytics. Data mining plays an essential role in providing aid to the predictive analysis by offering a record or history of the past. Moreover, data mining becomes necessary to predict which consumers are most probable to purchase or renew related services and products (Web Mining Data.net, n.d.).

Association Discovery

The use of association analysis can be an efficient way for identifying the behavior of specific process or event. An example can describe the association discovery through predicting that men who purchase best brand of coffee are 3 times more probable to buy imported cigars as compared to those men who buy ordinary coffee brands. Following are examples of association analysis:

If the consumer buys the snacks then there is 85 percent probability for buying soft drink or beer.

If the consumer buys airline tickets for spending their vacations with their family then there is 95 percent probability that the consumer will hire a car on rent.

Supermarkets and retail stores are eligible for creating a hypothesis for accomplishing their buying trends and patterns by using record of their data through employing data mining in their grocery stores (Moss, 2003). Data mining's effective results include 2 essential factors that are data size and query complexity. For a large amount of data, result will be more reliable and accurate (Data Mining, 2011).

Web Mining

Web mining is the method which is being used for the function of data mining. Mining and determining useful knowledge and information by page content, usage data and web hyperlink content is the key purpose of web mining. Web mining uses several techniques or methods for achieving ...