Data Mining

Running head: DATA MINING

Data Mining and How It Relates to Information Systems
Carmelisa Cummings
Strayer University

Abstract

Data mining is the process of analyzing data to remove information not offered by the unprocessed data alone. Data mining systems sort instantly through the information to discover patterns and relationships that would elude an army of human researchers. Data-mining tools relate algorithms to information sets to discover inherent trends and patterns in the information, which analysts use to create new business strategies.

Data mining is exploring and analyzing detailed business transactions. It implies "digging through tons of data" to uncover patterns and relationships contained within the business activity and history. Data mining can be done manually by slicing and dicing the data until a pattern becomes obvious. Or, it can be done with programs that analyze the data automatically. Data mining has become an important part of customer relationship management (CRM). In order to better understand customer behavior and preferences, businesses use data mining to wade through the huge amounts of information gathered via the Web. (Answers.com, 2009)

How does data mining work?

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: • Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically