Data mining provides a way for a computer to learn how to make decisions with data. This decision could be predicting tomorrow's weather, blocking a spam email from entering your inbox, detecting the language of a website, or finding a new romance on a dating site. There are many different applications of data mining, with new applications being discovered all the time.
Data mining is part of algorithms, statistics, engineering, optimization, and computer science. We also use concepts and knowledge from other fields such as linguistics, neuroscience, or town planning. Applying it effectively usually requires this domain-specific knowledge to be integrated with the algorithms.
Most data mining applications work with the same high-level view, although the details often change quite considerably. We start our data mining process by creating a dataset, describing an aspect of the real world. Datasets comprise of two aspects:
The next step is tuning the data mining algorithm. Each data mining algorithm has parameters, either within the algorithm or supplied by the user. This tuning allows the algorithm to learn how to make decisions about the data.
As a simple example, we may wish the computer to be able to categorize people as "short" or "tall". We start by collecting our dataset, which includes the heights of different people and whether they are considered short or tall:
|
Person |
Height |
Short or tall? |
|---|---|---|
|
1 |
155cm |
Short |
|
2 |
165cm |
Short |
|
3 |
175cm |
Tall |
|
4 |
185cm |
Tall |
The next step involves tuning our algorithm. As a simple algorithm; if the height is more than x, the person is tall, otherwise they are short. Our training algorithm will then look at the data and decide on a good value for x. For the preceding dataset, a reasonable value would be 170 cm. Anyone taller than 170 cm is considered tall by the algorithm. Anyone else is considered short.
In the preceding dataset, we had an obvious feature type. We wanted to know if people are short or tall, so we collected their heights. This engineering feature is an important problem in data mining. In later chapters, we will discuss methods for choosing good features to collect in your dataset. Ultimately, this step often requires some expert domain knowledge or at least some trial and error.