What are the Different Types of Data Mining Techniques?

Data mining generally refers to a method used to analyze data from a target source and compose that feedback into useful information. This information typically is used to help an organization cut costs in a particular area, increase revenue, or both. Often facilitated by a data-mining application, its primary objective is to identify and extract patterns contained in a given data set.

Most importantly, data mining techniques aim to provide insight that allows for a better understanding of data and its essential features. Companies and organizations can employ many different types of data mining methods. While they may take a similar approach, all usually strive to meet different goals.

The purpose of predictive data mining techniques almost always is to identify statistical models or patterns that can be utilized to predict a response of interest. For example, a financial institution might use it to identify which transactions have the highest probability of fraud. This is the most common method of data mining and one that has become an efficient decision-making tool for mid- to large-sized companies. It also has proven effective at predicting customer behavior, categorizing customer segments, and forecasting various events.

Summary models rely on data mining techniques that respond accordingly to summarized data. For instance, an organization might assign airline passengers or credit card transactions into different groups based on their characteristics extracted from the analytical process. This model also can 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 purchasing of items such as a mouse and keyboard or a sequence of events that led to the failure of a particular hardware device. Association models represent data mining techniques used to identify and characterize these associated occurrences.

Network models use data mining to reveal data structures that are in the form of nodes and links. For example, an organized fraud ring might compile a list of stolen credit card numbers, and then turn around and use them to purchase items online. In this illustration, the credit cards and online merchants represent the nodes while the actual transactions act as the links.

Data mining has many purposes and can be used for both positive and malicious gain. More organizations are coming to discover the benefits of merging data mining techniques to form hybrid models. These powerful combinations often result in applications with superior performance. By integrating the key features of different methods into single hybrid solutions, organizations usually can overcome the limitations of individual strategy systems.