A Quick Guide to Data Mining

By |2021-07-13T14:40:47+00:00June 11th, 2019|

This blog explains everything about data mining starting with, what is data mining with examples, what is the data mining process, and the importance of data mining. Lastly, we will discuss the various data mining techniques and how to select the best data mining tools for your business.

What is Data Mining with Examples?

Data mining is the process of analyzing large sets of data and deducing useful results from it. As operations grow and businesses become more complex, it becomes difficult for large enterprises to deduce useful information from large data sets. This complexity of dealing with big data has led to an increase in the popularity of data mining, thus, resulting in an increase in the use of data mining tools in an attempt to look for hidden patterns in the data. Some common everyday life data mining examples would be stock market analysis, online shopping, fraud detection, and financial banking. This has increased the use of database mining tools.

The data mining process uses mining algorithms on data assembled in data warehouses to identify hidden patterns and uncover valuable findings. Data mining has become an integral part of data sciences and benefits businesses, with organizations investing more time and money in the selection and usage of different tools used for data mining.

 Data Mining Techniques

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Understanding the Difference Between Data Mining and Data Integration

It is important to make a conceptual differentiation between the concept of data integration methods and data mining. Data integration is the process of combining, cleaning, and presenting data in a consolidated format. This includes unifying data from multiple, different source systems with disparate formats, eliminating duplicates, cleaning data according to business rules, and transforming it into the required format.

On the other hand, the purpose of data mining is to focus on finding patterns and relationships hidden in large data sets using efficient mining tools. The development of data mining projects requires the knowledge of statistics, machine learning algorithms, and database systems. The goal of data mining is to use advanced analytics and data mining algorithms, with the help of data mining tools, to make data usable.

How Does Data Mining Work? Why Use Data Mining?

The major question is, when does one use data mining? Data mining is used by businesses to gain intelligible insights out of their data, whether it is open source data or not. However, the data mining process is an extensive one, which requires the combination of a number of steps. The data mining process differs from use case to use case and company to company but this data mining guide will explain the process in a simple and basic manner. The answer to the common question “how many steps are in data mining” is that there are seven major steps in data mining. The following steps help users gain clarity on how to start the data mining process using robust mining tools.

Selecting Data

The first step in the data mining analysis process is to select the data sources that can be used to mine and get valuable information.

Extracting Data

The next step in the data mining process is data collection and extraction. A data scientist identifies the data sources, analyzes the sources, and uses integration flow to consolidate useful data.

Transforming Data

Once collected, data from different sources and different formats must be converted to a common format for it to be usable.

Cleansing Data

After data is transformed into a common format, it must be cleansed in order to ensure that the data is error-free, consistent, and unique. Data cleansing involves minimizing redundancy of data, manipulating data, organizing data, and applying governance policies to make the data meet compliance standards.

Storing and Managing Data

The next step is to store and manage data across different data warehouses in accordance with the type of data. Data can either be transactional, non-operational, or metadata.

Transactional data, which includes day-to-day operations, is stored in a separate location from non-operational data. Metadata is concerned with logical database design and is also handled separately. The stored data is then made available to business analysts using application software.

Analyzing and Mining Data

After data has been collected and loaded to a data warehouse, database mining tools are used to start the process. Mining and analyzing requires a combination of business intelligence and data mining algorithms. Understanding the business makes it easier for data scientists to produce a data mining model for data analysis. The question then arises- what is a data mining model?

A data mining model is created by applying different algorithms to data. Every data mining algorithm involves the process of identifying trends in a set of data and using the output obtained to define parameters. These parameters are then used to carry out descriptive analytics, diagnostic analytics, prescriptive analytics, risk management, or predictive analytics. The above model can be applied to multiple data mining examples such as in the financial investment industry.

Visualizing Data

After obtaining the results from the data mining process, it is necessary to ensure that the data is visually represented in an understandable form. Data visualization allows businesses to showcase the results generated by using data mining algorithms using charts or infographics.

Data Mining Applications

Data mining has useful applications in different industries, such as:

  • Healthcare: Robust data mining tools can be used in the healthcare industry to reduce costs, detect fraudulent activities,  and improve patient outcomes.
  • Education: The use of data mining tools in education can aid different aspects in the education industry, such as identifying how to encourage the leaning-needs of students, predicting how certain students will perform in examinations, and making efficient operational decisions.
  • Customer-Relationship Management (CRM): Data mining in business use can help analyze the customer data in order to help a business take customer-centric strategies and build successful,  loyal, long-last relationships with their clients or customers.

Best Data Mining Techniques with Examples

Depending on the data mining needs of a business, some common data mining steps are put into use. Following are the different types of data mining techniques with examples:


Association is one of the most commonly used data mining tasks. It is related to tracking patterns and relationships between dependently linked variables. Association hence looks for specific events or attributes that are correlated with another event or attribute. For example, when customers buy a specific item, businesses might notice that users tend to buy a correlated second or third item.


Classification is another data mining technique that requires organizations to collect various attributes into discernible categories. For example, data mining can help classify customers in the “low,” “medium,” or “high” credit risks category by analyzing their purchase history and financial background.

Tracking Patterns

Tracking patterns is one of the basic data mining techniques. It generally involves the recognition of some set of data happening at regular intervals. For example, mining tools can help a business notice that a certain product is sold more before a certain festival.

Outlier Detection

Another technique of data mining involves the detection of anomalies. Simply tracking patterns or classifying data cannot always be sufficient to understand your data set. For example, a business can notice a strange spike in female customers in an otherwise male-dominated sale item. The investigation of the spike and the reason behind it is an outlier detection process that makes businesses understand their customers better.


Regression is a method used to identify the exact relationship between two or more variables of a data set. An application example of the data mining regression technique is setting the price of a certain commodity based on customer demand, availability, and competition.


Clustering is similar to classification, but it involves chunking of data based on the similarities between the data sets. For example, a business can chunk different demographics of their audience into different packets depending on their income.


Prediction is one of the most valuable data mining techniques that allows you to project the types of data you might see in the future through predictive modeling. For example, it allows you to predict the sale of a customer based on their previous purchases, credit histories, and financial status.

Guidelines for Choosing the Best Data Mining Tool

data mining tools

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What are data mining tools? Simply put these are tools that assist in finding anomalies and patterns within a set of data. Get a free data mining software trial offered by Astera and start working on your use case The data mining tool you need depends on your business type, the data mining method or data mining technique that you want to implement, and sample data size. Some data mining tools use visual programming mechanisms and machine learning to give desirable results.





There are a number of popular data mining tools that you can use to meet your data mining needs. However, it is important to take the following considerations of characteristics of data mining tools, such as:

Amount of Data

Data mining tools you select must be capable of handling the amount of data you manage on a daily basis. If you process a huge amount of transactional data, it makes sense to buy a high-performance data mining tool. If your data set is not huge, a free data mining solution can be a suitable choice to fulfill your data mining requirements.

Human Resources

Using data mining tools also depends greatly on the resources that you have on hand. If you have data analytics and mining experts on your team, it might make sense to ditch the idea of utilizing data mining tools completely. On the other hand, if your team lacks technical expertise, it makes sense to invest in a good data mining tool that can help automate the entire process.


What results do you need from your data mining activities? Do you want to predict future outcomes, detect anomalies, classify data, or track patterns? The data mining tool that you select also depends on the results that you desire and the kind of organization that you are.

Different data analytics tools carry out data mining differently. It is necessary to choose the right data mining tool in accordance with the results that you require.


Price is another important consideration that can help you choose a suitable data mining tool. Choose a free data mining tool that requires you to pay after the trial period. Also, choose a pricing model that meets your organizational needs.


Choose a data mining tool that offers 24*7 support and adequate, easy to follow documentation.


A data mining tool that does massive computations but cannot visualize the results is not suitable for any business. Choose a data mining tool with excellent graphical illustrations.

Ease of Use and Upgrade

Choose tools used for data mining that is easy to use, have a natural learning curve, and offer regular upgrades. A good data mining software provider upgrades its product regularly with changing business needs.

Possibility to Work on the Cloud

Depending on your organization’s size, the possibility to work on the cloud is another added benefit that is inevitably important when it comes to accessing data from online data sources.

In some cases, you might need the combination of more than one data mining tool, one for visualization purposes and one for collecting data and carrying out computations.