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10 Data Mining Techniques: The Complete List

The truth is that we have access to more data now but yet we struggle to put it to effective use. Being surrounded by such big data, organizations are more lost than ever as it’s challenging to implement all of it, given the size of the information that is collected. And with that, it has to lead to many unstructured data and what we need is the right type of information that will boost the company.

So this is where having powerful data mining techniques to curb such data is a necessity. The various techniques listed below will give you a better understanding of why we need them and why it should be implemented. However, with all that in mind, it is also important that we understand how data mining vary and caters to different issues and different kind of solutions. In the present era, we are surrounded by data that is forecasted to grow 40% per year into the next decade.

Data mining is a combination of these three steps: Exploration, Modeling, and Deployment.

Data Mining Process

Data Mining Techniques:

1.   Classification

Classification is one of the most used data mining techniques as it is used for analyzing various characteristics that are associated with different kinds of data. Then, classifying those data into sub-classes and categorizing them based on the information collected.

For example, you can use this technique to identify the number of male and female workers in your organization by simply classifying them to List 1 for male employees and List 2 for female employees. It is also used in developing software that will make classifying items of data set into various classes.

2.   Clustering

Clustering is often confused with classification, so we must have the differences checked. It is nothing but identifying data that are similar to each other. It then helps them to recognize the similarities and the differences between the data. We often refer to it as segmentation as it helps users to understand what exactly goes in with the database.

3.   Prediction 

Prediction is nothing but a technique that will help you in understanding the data that we will possibly see in future events. It uses patterns that are found in current or historical data to extend them further in the future, giving the organization an insight into prospective future trends. We are never sure about whether our investment, time, and money are in the correct place protected by the correct methods. So what prediction analysis does is that it studies the previous or present events, to analyze almost correct predictions for the future.

For example, you can introduce an item in the market, and based on its sales profits, you can simply predict how much profit it will garner in the future.

4.   Statistics

Statistical data mining technique is a branch of mathematics that is related solely to the collection and the description of data. Many data analysts rarely consider statistics as a data mining technique. However, it has helped in building predictive models as well as discovering patterns.

5.    Outlier Detection 

Outlier detection is a type of data mining technique that observes the data items present in a dataset which has deviated from its expected pattern and behavior. Once irregularity is detected, it makes it simpler for organizations to understand why the anomalies happened in the first as well as prepare them for future happenings to achieve better business results. This technique is also valuable in network disturbance, credit or debit card frauds, etc.

6.    Associations 

This data mining technique is a technique that helps in finding an association between two or more items and helps to find hidden patterns that can help in the identification of variables within the data. This is most popular in the retail industry to check the pattern in sales.

7.    Decision Trees

This is one of the simpler data mining technique to understand and put to use. It is part of machine learning and is a predictive model that enables users to know how data inputs affect the output. From the name itself, we understand that it resembles a tree, and each branch is seen as a classification and the leaves are a partition of the dataset that is about classification. A bigger percentage of the statisticians use a decision tree to determine which database will give them the answers to their problems.

At the root of any decision, the tree is a question box with many variable answers. And based on those answers, we can derive the final response to the central question.

8.    Regression 

Regression analysis is a data mining technique that identifies and analyzes the relationship among the various variables in a dataset. The goal of regressing is to show the relationship between two variables in one set and also to show how one variable can be dependent on another, but it’s not vice versa.

9.    Sequential Patterns

This technique helps to uncover events that have taken place in the sequence. This is one of those data mining techniques that work a great deal for businesses in identifying their sales pattern.

10 . Visualization

Data visualization is a technique that allows users to get an insight into data in various forms, like charts, diagrams, digital, etc. This helps in strategically calculating and drafting their growth. Not only that, but it also allows them to check the market for any kind of competition that is there so that they can up their chances for a better position.

This data mining technique helps businesses to make a more refined decision.

“Signals always point to something. In this sense, a signal is not a thing but a relationship. Data becomes useful knowledge of something that matters when it builds a bridge between a question and an answer. This connection is the signal.”

― Stephen Few, Signal: Understanding What Matters in a World of Noise

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InfoCleanse (Sam Wilson)