13 Data Mining Techniques every Data Scientist should Know

by Manish Singh | Updated on: October 21, 2020 | 7 Min Read.

Data mining techniques are used for extracting intelligence from a huge source of information. 

They revolve around the process of examining a dataset for extracting certain patterns. In the process, automatic sifting occurs through the large volumes of data to find-out patterns with the help of different tools like clustering, association rule mining, classification, etc. 

Companies use these techniques for analyzing the outcome of their existing goals. All of their financial models, risk analysis, and project management involve dealing with huge chunks of data. A lot of data mining concepts and techniques can come in handy for the same. 

The sheer amount of information that data analysis requires makes sense of such humongous data- both structured and unstructured. This data needs proper analysis with the help of data mining techniques, as it comes in handy to implement organizational improvements. In case the analysis is not up to the mark, it can lead to the data becoming extremely inefficient.

There are many techniques of data- mining is popular today that this article will unfold for you. However, before delving into those, let us first understand what data mining techniques are and why they are highly crucial-

What are Data Mining Techniques?  

Data Mining techniques refer to the set of processes by which organizations detect patterns in data to obtain rich insights into their business for growth, analysis, and development. 

It comes in handy both as business intelligence as well as data science courses. There are numerous data mining concepts and techniques that businesses use to turn the raw data into actionable insights. It involves all the mining techniques from data preparation to artificial intelligence. These both form the core of data science courses to maximize the value of investments in data science techniques. 

Data scientists, like their software engineer counterparts, indulge in the business side of data science.

They analyze the data using different data science techniques, and the results are then understandably shared with businesses. Data Science Courses ponder upon the individuals’ ability to verbally and visually communicate to prove complex results using a set of data mining techniques. 

Working of data mining is based upon consumers concerning both-

  • Internal Factors like price & product positioning
  • External Factors like competition & demographics

These factors play a crucial role in determining consumer price, corporate profits, and customer satisfaction. Four different types of relationships that are associated with data mining-

  • Classes – Associates with the info used for increasing traffic
  • Clusters – Information is grouped for determining consumer preferences or logical relationships 
  • Associations – Revolves around grouping products that are normally bought together 
  • Patterns – Use for anticipating the behaviour trends

Why is Data Mining important? 

The need for data mining concepts and techniques cannot be undermined in a complex, analytics-driven world today. 

Let us have a look at the reasons essential for knowing Data Mining concepts and techniques: 

  • The Tech industry is overflowing with the need for intricate analytical talent today. 
  • If your career options include Data Science or Artificial Intelligence or Predictive analysis, this can be a good skill set. 
  • You can discover human-interpretable patterns using data science techniques. 
  • You can use the variable to predict the unknown in the future of businesses. 
  • It’ll add a lot of value to your existing knowledge of algorithms, data scalability, and data automation. 

11 Data Mining Techniques every Data Scientist should know

Now that you are familiar with the basics of Data Mining Techniques following are the important Data Mining concepts and techniques that are useful to any data scientist. While taking up any Data Science Course, one should focus primarily if these points are included. 

1) Cleaning and preparation of data 

One of the first and most important steps of Data Mining techniques is the cleaning and assembling the data so that it is ready for analysis. It includes different elements of data modelling, migration, data transformation, integration, and aggregation.

It becomes important to understand the basics of the data at hand. The business-value of this step is evident. Without cleaning, the data shall be raw and inaccessible. Companies should be able to trust the data and its quality to have their confidence in the analysis. Thus, among all complex data mining concepts and techniques, this one is very important. 

2) Tracking the patterns in the data

Tracking patterns in the data form the core of data science courses. The main components of tracking patterns as a data mining technique include identifying and monitoring trends to develop intelligent references to aid businesses in taking risks. 

For example, a pattern occurs here in which a certain quality product is selling more than other products; a business can use it to invest in higher selling products. 

3) Classification of data 

After finding out the trends and patterns in the data, classifying it is next in data-mining techniques. The data can get the systematic organization in categories after tracking to simplify its accessibility. 

In case the business organization wants to scrap or add a particular category of data in the future, it becomes easier with categorization. This technique is simple and forms an important base for Data Science courses.

4) Data Association

The Data Association finds its relation to statistics. It helps trace how certain data events are linked to other data-driven events. This data-mining technique is similar to the correlation in statistics or co-occurrence in machine learning. 

It shows how two events might be interlinked. For example, the purchase of burgers has its interlinking to the purchase of a soft-drink. 

5) Outlier Detection

Another important component of data analysis is looking for anomalies and scrapping them. 

Once the organizations find out the aberrations using complex data mining concepts and techniques, it becomes easier to rework upon them. This shall ensure that similar errors do not spike up in businesses in the future. 

6) Data Clustering

Clustering is a data mining technique that relies on visual approaches to understanding the data. 

It uses graphics to understand the skewness in data distribution using different matrices. The ideal cluster analytics use graph approaches. The observation of trends in the data becomes easier if the data is in a visual form. 

7) Regression 

Regression deals with identifying the relationship between the variables in a data set. These might be casual or correlational. 

This Data Mining technique is a straightforward white-box technique used for data modelling. It has been quite useful in forecasting and data modelling.

8) Prediction of trends

Prediction forms one of the most important branches of data analytics. Using different data-mining techniques, the predictive analysis uses historical trends to extend them into the future. 

The evolved versions of predictive analysis are Machine learning and artificial intelligence. 

9) Sequential patterns 

Sequential patterning is an interesting component among other data mining concepts and techniques. 

It helps uncover a set of events in the data that are occurring in one particular sequence. This is important for businesses to recommend additions to their already existing purchases. 

10) Decision trees of data

This is a white-box data mining tactic. It helps the businesses understand how the input of the data affects its output. 

When the assembling of many such trees occurs, it is known as a random forest in machine learning. Although this technique’s reliability is less due to the varying nature of the data input, it is still handy to understand how data is behaving. 

11) Statistical techniques and Visualization 

Statistical techniques and the ability to visualize data models are the basis of practical knowledge of data mining techniques. All the above operations on data become impossible without adequate knowledge of statistical tools. 

Dashboards have also come into the forefront to have visual data. Different business organizations can base their dashboards on various metrics to stream data in real-time. This can be done using different colour schemes revealing different trends and patterns in the data. 

12) Distance Measures

One of the common data mining issues is to find out data for “similar” items, for instance finding almost duplicate pages from the collection of webpages. 

Distance Measure is a useful data mining technique for dealing with the problems associated with finding near-neighbours in any high-dimensional space. 

13) Neural Networks

Neural networks are one of the very effective data-mining techniques and their usages are quite common with AI and deep learning. 

As one of the most accurate machine learning models, Neural networks work in the same way as neurons work. 

Final Thoughts about Data Mining Techniques!

The importance of data mining techniques for businesses today is inevitable. 

Organizations can start tapping different data science courses to optimize these data analytics tools to fit their use. The modern forms of Data warehousing are crucial in this regard. This aids organizations to develop insightful predictions to increase their outreach and sales in the future. 

Using a single tool to perform these procedures ensures that companies have one place to implement these data mining techniques and reinforce the data quality. 

To learn more about such techniques, advanced data science, and machine learning,  enrol in a data science course now!

Do you want to add some other mining techniques to our list? Feel free to share those in the comments.

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