Data Mining 101: Listing Down Best Techniques

April 25, 2019 Business, Marketing

Most often people think that data mining refers to the extraction of some sort of ‘new data’. I thought the same when I heard this word the first time. However, it is something different. Basically, it involves anticipation and extrapolation of already saved data, for in-depth analyzing and generating new information and patterns for your business enhancement.

8 Major Data Mining Techniques

Data mining techniques have originated with the intersection of database management, machine learning, and statistics coupled with artificial intelligence. It took hardships and a lot of efforts for data mining experts to understand the process and make statements from huge data packets. You can check the brief history of data mining over the internet.

Let’s look at some of the most important data mining techniques.

1. Tracking – Identifying the Data Sets Patterns

It is the most fundamental techniques in data mining. Recognizing the patterns in the data set is the basic functionality of this particular technique. This is usually an acknowledgment of some deviation in your information occurring at specific intervals, or a back and forth movement of a particular variable after some time.

For example, you own an online shop and monitor the visitors landing aligned with the sales. Suddenly, you might notice that traffic and sales increased when the weather got hot. Similarly, consumers are demanding particular products during the long holidays. The analyzing and tracking skills will benefit you here for future planning.

2. Classifying the Details

Classification is progressively more typical data mining system that compels you to gather different characteristics together into functional groups. It helps you making further conclusions, or takes some actionable steps.

Let’s have a look at an example. Suppose that you are analyzing data for your customer’s financial background and the purchase history. You can classify it as low, medium or high credit risk. This will help you learn more about such customers.

3. Associating the Events

This technique is based on tracking the patterns. However, it is specified to the conditionally connected factors. It allows you to search for the attributes and cases in which a specified event is related to another.

It involves noticing the behavior of your customer like whenever they buy a particular item, they go for another related item. The origin of ‘people also bought section’ in stores is based on this technique.

4. Detecting the Uncertain Actions

It is for sure that analyzing the patterns of your data set is just not enough. You need to be prepared for the unexpected deviations and unusual behavior of your industry.

For example, your sale for the last week of every month goes down. But, it increases in December way high. You’ll need to investigate and understand the spike and look for what drove it. The technique will help you better prepare for the next unexpected happenings.

5. Cluster Analysis

This particular technique is based on identifying the similarities and differences of behavior between the data. It is more linked with classification. However, this technique, in particular, is based on very in-depth analysis.

Let us discuss a case here. I was working previously as an academic advisor and career counselor with MHR Writer – a student helping portal. During their initial 5 years of service were unable to lift their progress. However, then they started cluster analysis and focused on the different sort of assignments student want them to assist with. This helped them realized, where did they lack, i.e., delivering with dissertations. They fixed the issues and the next five year was tremendous.

6. Regression Based on Relation

Regression, utilized basically like a type of arranging and demonstrating data sets to distinguish the probability of specific aspects, based on the relation with another element. Let me make it more clear with an example. A company mostly increase the cost of their particular product based on demand, customer intentions, and competition.

The particular technique is focused on giving you the exact relationship between two or more variables in a data set.

7. Predicting the Expected Picture

Since the technique let you understand the future expects of your industry, this techniques is worth more than any other. It is used to project the nature of data you’ll handle in the near future. Based on previous behavior and historical trends it somehow manages to chart a picture of what will happen next.

8. Creating Decision Trees

Decision trees help you answer any particular issue faced. Its root is always a question that is further divided into several answers. Based on your data and previous trends, you can answer the question and handle the issue. It helps you analyze your data keenly.

Why Not Start Data Mining Now?

You might be assuming before reading the article that it might require machine learning and AI to understand the techniques and apply them. But that’s not the necessity. Make it simple using database systems and tools you already have. If utilized correctly these techniques will help you to revolutionize the enterprise through you potential conclusions. If you are a startup, check these data mining techniques for startups.

About the author:

Stella Lincoln is a mother of two cute daughters. She spends her most of the time with AcademistHelp where she works as an SEO Analyst. Apart from that, Stella is a computer system tutor at Crowd Writer. She has a great passion for writing, and in her leisure time, you will find her sharing her thoughts and expertise on Educator House.