7 of the most common data mining techniques

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Data mining is a process used by companies to turn raw data into useful information.

By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection, warehousing, and computer processing.

What are the benefits of Data Mining?

  • Data mining helps companies to gain knowledge-based information.
  • Data mining helps organizations to make profitable adjustments in operations and production.
  • Itis a cost-effective and efficient solution compared to other statistical data applications.
  • Helps with the decision-making process.
  • Facilitates automated prediction of trends and behaviours, as well as the automated discovery of hidden patterns.
  • It can be implemented in new systems as well as existing platforms.
  • It is a time-efficient process which makes it easy for the users to analyze huge amounts of data in less time.

The Disadvantages of Data Mining.

  • There are ethical concerns as there is a chance a company may sell a customer's useful information to other companies for money. For example, American Express has sold credit card purchases of their customers to the other companies.
  • Many data mining analytics software is difficult to operate and requires advance training to work on.
  • Different data mining tools work in different manners due to different algorithms employed in their design. Therefore, the selection of the correct data mining tool is a very difficult task.

Now that we understand the benefits and disadvantages of data mining, let's take a look at seven of the most common techniques that can be used to mine valuable data.

Most Common Data Mining Techniques.

1. Prediction.

Prediction is one of the most valuable data mining techniques since it’s used to project the types of data you will see in the future. In many cases, just recognizing and understanding historical trends is enough to chart a somewhat accurate prediction of what will happen in the future. For example, you might review consumers’ credit histories and past purchases to predict whether they will be a credit risk in the future.

2. Tracking patterns.

One of the most basic techniques in data mining is learning to recognize patterns in your data sets. This is usually recognition of some aberration in your data happening at regular intervals or an ebb and flow of a certain variable over time. For example, you might see that your sales of a certain product seem to spike just before the holidays or notice that warmer weather drives more people to your website.

3. Classification. 

Classification is a more complex data mining technique that forces you to collect various attributes together into discernable categories, which you can then use to draw further conclusions or serve some function. For example, if you’re evaluating data on individual customers’ financial backgrounds and purchase histories, you might be able to classify them as “low,” “medium” or “high” credit risks. You could then use these classifications to learn even more about those customers.

4. Clustering.

Clustering is very similar to classification but involves grouping chunks of data together based on their similarities. For example, you might choose to cluster different demographics of your audience into different packets based on how much disposable income they have or how often they tend to shop at your store.

5. Association.

Association is related to tracking patterns but is more specific to dependently linked variables. In this case, you’ll look for specific events or attributes that are highly correlated with another event or attribute. For example, you might notice that when your customers buy a specific item, they also often buy a second, related item. This is usually what’s used to populate “people also bought” sections of online stores.

6. Regression. 

Regression, used primarily as a form of planning and modelling, is used to identify the likelihood of a certain variable, given the presence of other variables. For example, you could use it to project a certain price, based on other factors like availability, consumer demand, and competition. More specifically, regression’s main focus is to help you uncover the exact relationship between two (or more) variables in a given data set.

7. Outlier detection. 

In many cases, simply recognizing the overarching pattern can’t give you a clear understanding of your data set. You also need to be able to identify anomalies or outliers in your data. For example, if your purchasers are almost exclusively male, but during one strange week in July, there’s a huge spike in female purchasers, you will want to investigate the spike and see what drove it. This way you can either replicate it or better understand your audience in the process.

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