Data mining facts for kids

Data mining is a cool part of computer science. It's also known as knowledge discovery in databases (KDD). Imagine you have a giant pile of information, like all the things people buy at a store. Data mining is like being a detective who sifts through that pile to find new, useful secrets hidden inside!

When businesses or people save information, they usually have a main reason. For example, a store saves what customers buy. They do this to know how much stuff they need to order so they always have enough to sell. This saved information often goes into a special computer system called a database. This first reason for saving data is called its "first use."

But later, that same information can be used for other things the store didn't think of at first. Maybe the store wants to know what items people often buy together. For instance, many people who buy pasta also buy mushrooms. This kind of information is super useful, even though it wasn't why the store first saved the data. Finding these new, helpful facts from existing data is exactly what data mining is all about! It's like getting a "second use" out of the same information.

How Does Data Mining Work?

Data mining uses different methods to find new information. Often, it tries to guess or predict things. These predictions might not always be 100% certain, but they are very helpful. Think of it like trying to guess what an apple will taste like just by looking at its size and color.

Finding Patterns

One way data mining works is by finding patterns. This means looking for similarities or rules in the data.

• Pattern recognition: This is like finding rules, such as "small apples are often green." Computers look at lots of data to find these hidden connections.

Smart Guessing with Networks

Other methods use special computer models that learn from the data.

• Bayesian network: This method helps us understand how different pieces of information are connected. For example, it can show how an apple's size might affect its color. If you know the size, you can make a good guess about the color.
• Neural network: This is a bit like how our own brain works. You give the computer lots of examples, and it learns from them. For instance, you could teach it that green apples often taste sour. Even if we don't fully understand *how* it makes its guess, it can still tell us that a green apple has a higher chance of being sour. It's like a "black box" that just works!

Decision Trees

• Classification tree: Imagine you have an apple and you know its size, color, and if it's shiny. A classification tree helps predict something else about it, like what it will taste like. It uses all the information it has to make the best possible guess.

See also

In Spanish: Minería de datos para niños