Data mining is a process based on discovering patterns.

This is why you need to create individual tasks that will help you search through data and perform proper classification and data normalization. These data mining techniques or methods help you bring some order to web data as you try to find patterns

You can find a lot of science books online that will help you learn more about mining data and how it can benefit your business. You can also choose between various software and applications that can automate this process.

Issues with your database or databases, noise as well as some other problems can all affect the results.

Regardless of your management policy, you probably realize how data mining can improve your website, content and business in general.

In order to understand how this process works, let’s go through the individual data mining techniques and common problems.

1. Association

Association is one of the main ways to identify patterns.

During this process, you will find a common link between two or more items. This occurrence should help your prediction.

The best way to explain is like this: people who watch a whole video on YouTube made by a certain author are likely to view another video of the same author.

It is especially important for predicting customer behavior. It can be used for different product analysis including shopping cart analysis and to help with any customer-related decision. It is widely used for discovering trends and it can also provide us an advanced knowledge of an industry.

2. Statistical techniques

This method mainly pertains to statistics management.

Depending on which author you ask, some people will tell you that this isn’t a real data mining technique as it is isn’t directly involved in extraction process. Still it is very important in order to access predictive models.
With statistics, you can view trends and get a new perspective on acquired data.

I would also like to add that statistical data mining techniques are being used in number of scientific fields.

3. Classification

With this data mining technique, you are able to classify acquired data.

Please have in mind that classification is heavily reliant on algorithms; by setting up initial parameters you are able to classify your data.

For example, you can classify beer based on its appearance, aroma flavor etc.

Each classification model type is specific. Here is a simple way to segregate them:

• Classification by decision trees induction
• Bayesian Classification
• Neural Networks
• Support Vector Machines (SVM)
• Classification Based on Associations

Classification is usually based on one particular attribute. While two items can belong to a same class based on a specific attribute, all other attributes can be different between them.

4. Clustering

During clustering, data with same or similar characteristics is placed within a group (clusters).

However, placing data within clusters is not the only reason why we are doing it; we are also interested in the relationships between these clusters.

During clustering we start grouping data based on their similarities.

First, all this data is placed within a chart with two main criteria. Pieces of data that have similar values are grouped within one cluster. Similarly, we can create other clusters. Now, at the end of the analysis we will have several clusters that can be used for analysis.

For example, we can compare incomes of people doing the same job.

First criteria that we use it work time while the second one is income.

People who work an average of 40 hours per week will have an average wage for that industry. People who work 50 hours per week will have higher income.

Although there will be some inconsistencies, we will be able to generate two major clusters based on this data.
In terms of its usefulness, clustering has a wide application.

Clusters allow us to group data and asses situations. It allows us to generalize things and understand interaction between data.

5. Regression analysis

Regression is an analysis that allows us to predict an outcome based on real-value variable.

We use regression to predict value of an individual features based on other data that is available to us. For example, if you invest 1 million dollars in real estate and average return on investment is 10 % you can presume that your profit will be around $ 100.000.

When using this technique, we rely on linear and nonlinear models.

Regression is generally used as a predictive function, helping us determine possibility of an outcome based on past data. It can also be used during learning and analysis process where we can choose one of several options.

6. Anomaly detection

As the name implies, anomaly detection pertains to a technique that helps us find anomalies within a data set.
Anomaly refers to anything that is well above or below expected value. When performing this analysis, not only are we able to establish deviation from the average value but also to discover a percentage of data that deviated.

Anomaly detection can be used in insurance, education, health etc.

It is especially important for future subsequent procedures or actions as it helps us understand how often the data deviates and what extremes we can expect.

Last thoughts

As you can see from this text, there are various ways to perform data mining.

Each of these techniques provide new results and show us some new possible patterns. Although nowadays you can download various tools to help you out with the process, big companies perform their data mining themselves.

This is mostly due to the fact that when it comes to obtaining data, one size doesn’t fit all. The process requires a lot of micromanagement and modification in order for you to find patterns you need.

There are no rules when it comes to which technique is ideal for your site; in fact, you might find several of them to be valuable.