Data Mining Techniques (Part 1)
Businesses rake in more data than ever before, simply because more data is being produced than ever before. Besides, organizations have more access to data through multiple sources like social media platforms, public databases, etc.
While there is more to go around for Business Intelligence (BI) than ever before, it is still difficult to make sense of large volumes of data. The sheer amount of data makes storage, security, maintenance, and data analytics somewhat challenging, whether structured or unstructured.
If these things aren’t done correctly, the BI benefits of data analytics may be compromised. This is where data mining can help immensely. It is the process that allows businesses and organizations to process raw data and turn it into meaningful information.
They can detect patterns in the data using data mining, which proves insightful for their business. Turning raw data into actionable insights does not require one particular method. Instead, plenty of data mining techniques can be used for this purpose.
There are so many that we have split these techniques into two parts for our topic of discussion today – Data mining techniques. Here we will discuss the more common and basic ones like data cleaning, clustering, and regression.
The next part will discuss more complex techniques like sequential patterns, machine learning, and Artificial Intelligence (AI).
So, let’s get started.
Data Mining Techniques (Part 1)
Here are some common and basic data mining techniques.
- Data Cleaning Techniques
Data cleaning is a crucial part of any data mining effort. It is the first step in preparing raw data for valuable data analytics. With this data mining technique, collected raw data is cleaned and formatted for proper storage and use.
Various elements are at play, including data migration, transformation, modeling, ETL/ELT (Extract, Load, Transform), integration, and more. Data cleaning reveals the primary attributes and features of the raw data, which are essential to determine the data’s best use case.
It is an invaluable first step because, without data cleaning, the raw data is practically worthless and proves unreliable for BI and data analytics because of its poor quality. Businesses need to maintain data quality for actionable insights, which is why this data mining technique is so important.
- Pattern Tracking Techniques
Tracking patterns is another essential data mining technique, where businesses identify and observe patterns or trends in the data to make clever deductions and decisions about business outcomes. For instance, if your business recognizes a pattern in your marketing data, you have reason to take useful actions regarding this insight.
Let’s suppose your marketing data indicates that a particular campaign is doing well for target audiences. You can use this insight for future marketing campaigns for similar audiences.
- Classification or Categorization Techniques
Classification data mining techniques involve the categorization of data. It entails analyzing the various attributes associated with different types of data. Once your business can identify the key characteristics of various data types, you can categorize or classify similar characteristics together.
This is crucial for recognizing various data types and their information. For example, it can help you categorize personally identifiable information about your employees or customers, which you may want to protect from exposure in documents.
- Association or Correlation Techniques
Association, or correlation, is a common data mining technique that refers to statistics. It indicates that certain data or information inside the data is “associated,” correlated, or linked to other data or data-driven events in your dataset.
This concept of association is very similar to the concept of correlation in mathematical statistics. Machine learning also has a similar concept, known as co-occurrence, whereby the existence of another indicates the odds of one data-driven event.
With association data mining techniques, you can learn about relationships between two data events. For example, the purchase of burgers often goes together with the purchase of French fries. You can learn this relationship between two data events through association techniques.
- Outlier Detection Techniques
As the name suggests, outlier detection is a data mining technique that finds any anomalies in datasets. By identifying these anomalies in the data, your business can better understand why they occurred and how it can better prepare for their future occurrences.
For example, if there is a boost in the sale of a product during a certain time of the year, your business can capitalize on this insight by learning why this happens and how it can optimize marketing or sales during the rest of the year.
- Clustering or Grouping Techniques
Clustering, or cluster analysis, is a data analytics technique that entails grouping a set of data in a way that that data in the same group is more similar or identical to each other than it is to data in different groups. Cluster analysis relies on a visual approach to comprehend data.
Clustering systems use visuals to show where the allocation of data is relative to various types of metrics. This can also be done using different colors to show data allocation. Cluster analytics is also ideally displayed with the use of graphs. Using graphs to display clustering allows businesses to visually understand the distribution of data and identify useful trends that are beneficial for business.
- Regression Techniques
Regression data mining techniques help identify the nature of relationships between variables in a dataset. These relationships may be correlations, or they could be causal. This will vary depending on the data and the datasets.
Regression is a white box technique that clearly and completely reveals how variables in a data set are related. This is different from black-box techniques, where you have the input and may get the outcome, but the process or relationship is not revealed.
Regression techniques are used mostly in BI and data analytic areas of data modeling and forecasting.
- Prediction Techniques
Prediction techniques are a powerful facet of data mining that represents one of four key branches of data analytics. Predictive analytics rely on patterns in your data (historical data) to expand them into the future and make accurate predictions.
Such prediction techniques give businesses great insight into what trends will occur next in their data. There are many approaches to using prediction techniques or prediction analytics. The more advanced ones entail using other branches of BI, like machine learning and AI.
However, these techniques do not rely solely on machine learning or AI and can be made possible through basic algorithms.
Learn More in Part 2
That’s it for now. You can continue to learn more about data mining techniques in the next part of this article, Data Mining Techniques (Part 2), where we discuss more complex data mining techniques.
You can also learn more about Business Intelligence, data mining, data analytics, machine learning, artificial intelligence, and how to deploy these technologies for your business by visiting our website today.
