Data mining is also known as knowledge or data discovery. In general, the standard data mining definition is this: the process of data analysis from various viewpoints and its subsequent summarization into information that be utilized by organizations for their specific needs.
When talking about the data mining definition, you should know that an array of tools for data analysis are used, which includes different data mining software options. These tools make users capable of analyzing data from a host of angles, properly classifying them, and summarizing all determined relationships. In the technical sense, the data mining definition can also be: the method of searching for patterns and correlations among a wide variety of fields in huge relational databases.
Data Mining Definition: How it Works
Data mining is mainly utilized by businesses with direct focus on consumers such as those in the retail, marketing, communication, and finance industries. It allows them to identify correlations among external factors like client demographics, competition, and economic signs, and internal factors like positioning products, price, and employee skills, etc. This in turn allows users to accurately identify profits, client satisfaction, and sales impact so they can summarize the findings.
The data mining process is made up of 5 key elements:
- Obtaining, converting, and loading data into a data warehouse.
- Storing and organizing data in a dimensional database model.
- Offering data access to users in an organization such as analysts and executives.
- Analyzing data by using data mining software.
- Presenting data in a more usable form like in a table or graph.
In relation to the more technical data mining definition, data mining software applications are created for the analysis of patterns and relationships in transaction data contained in data warehouses based on open ended questions made by users. These include neural networks, machine learning, and statistical software. Primarily,
these software applications look for any of the 4 relationship types:
1. Association – Let’s say women who buy baby food on Mondays and Thursdays also tend to buy laundry detergent.
2. Cluster – Items of data are grouped together based on consumer preferences or logical correlations which can be used to determine client inclinations and market sectors.
3. Class – Stored data can be utilized to look for data in preset groupings such that supermarkets can analyze client purchase information to identify favored items and their standard purchase period.
4. Sequential Patterns – Data mining is performed to predict trend forecasts and buying patterns.
Data Mining Definition: Application
To better understand the data mining definition presented above, here is an example of data mining at work. To illustrate, a supermarket makes use of data mining software in hopes of analyzing their customers’ purchase patterns. They found out that women who purchase baby food on Mondays and Thursday also tend to purchase laundry detergent and that they usually complete their weekly grocery shopping during Saturdays. They also discovered that these women only purchased a few products during Thursdays.
In this scenario, the supermarket owners concluded that these women bought laundry detergent to be used on weekends. This new information can then be utilized by the owners to increase their sales based on their clients’ purchase patterns. Because of data mining, businesses can utilize client purchase records for applying targeted promos according to their customer’s buying history. They can then create more consumer driven promos and products to appeal to particular market segments.