Data mining is a method used to analyze data and extract patterns to provide actionable information for organizations. Predictive, summary, association, and network models are different types of data mining techniques that can be used for various purposes. Hybrid models combining different techniques can result in higher performing applications.
Data mining generally refers to a method used to analyze data from a target source and compose that feedback into actionable information. This information is typically used to help an organization reduce costs in a particular area, increase revenue, or both. Often facilitated by a data mining application, its primary goal is to identify and extract patterns contained in a given data set.
More importantly, data mining techniques aim to provide information that allows for a better understanding of data and its essential characteristics. Businesses and organizations can use different types of data mining methods. While they may take a similar approach, they usually all strive towards different goals.
The purpose of predictive data mining techniques is almost always to identify patterns or statistical models that can be used to predict a response of interest. For example, a financial institution could use it to identify which transactions are most likely to be fraudulent. This is the most common method of data mining and has become an efficient decision-making tool for medium to large companies. It has also proven effective in predicting customer behavior, categorizing customer segments, and predicting various events.
Summary models are based on data mining techniques that respond accordingly to the summarized data. For example, an organization might assign airline passengers or credit card transactions to different groups based on their characteristics extracted from the analytics process. This model can also help businesses gain a deeper understanding of their customer base.
Association models take into account that certain events can occur together on a regular basis. This could be the simultaneous purchase of items such as a mouse and keyboard, or a sequence of events that lead to the failure of a particular hardware device. Association models represent the data mining techniques used to identify and characterize these associated events.
Network models use data mining to reveal data structures in the form of nodes and links. For example, an organized fraud ring might compile a list of stolen credit card numbers, then turn around and use them to purchase items online. In this illustration, credit cards and online merchants are the nodes while the actual transactions act as the links.
Data mining has many purposes and can be used for both positive and detrimental gains. More and more organizations are discovering the benefits of combining data mining techniques to form hybrid models. These powerful combinations often result in higher performing applications. By integrating key features of different methods into single hybrid solutions, organizations can usually overcome the limitations of single strategy systems.
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