Top 5 Popular Data Mining Techniques

With the advancement of technology and data being generated every day, there is an increment in the amount of data being generated per second. Terms like Big Data are becoming quite common nowadays owing to this high data explosion. It is forecasted that soon our world will converge into a Global village with data ruling over the world. To get useful information out of this huge set of data, Data mining process is done.

This process of data mining aids in producing new information from a large information set. Not only this, data mining is the technique of extracting some information out of the given data set. The data that has been collected already is quantified to take out some relevant information based on a particular pattern.

There are a plethora of data mining techniques that play a pivotal role in drawing relevant conclusions from a given data set. The profession of a data scientist is gaining more prominence as the need for classifying data from unstructured data is increasing day by day. They are striving hard in researching the right method that can process and make the information useful. Are you not intrigued to know how?

List of Top 5 Famous Data Mining Techniques

Data mining is known to be an effective method that involves the use of several techniques that allow easy identification of a pattern and implementing them for arranging data accordingly:

• Keeping a Track of different patterns:
  This is a fundamental technique that keeps collecting data at regular intervals of time and recognizes a set of patterns that form in it. This can be done by applying simple statistical techniques, finding the Median, Mode, Variance, Histogram, Mean, Max, and Linear Regression etc. Tracking mainly involves answering these questions in detail:

1. 1. 1. Identify the patterns available in the database?
      2. Find the probability of a particular event to occur?
      3. Which are the more useful patterns for the business?
      4. What would be the high-level summary that can provide you with a full view of contents in the database?
         • 

For Instance: if a business wants to check its sales report of a specific item over the last 3 months or when someone wants to check the number of average visitors coming to their website. Thus, there is a need for maintaining a proper detail of all such patterns in detail to track the data and provide a relevant conclusion.

• Classification Technique:
  It is said to be the most useful data mining technique that classifies data into different categories that can be used to draw inferences. For instance: if you are classifying student’s data on the basis of their grades like “A”, “B”, “C”, and “F”, then you will see that there is a large set of data that is created from an entire school or district making it a complex process.

The 2 main processes that are a useful part of this process are:

1. 1. Learning – You will find data being analyzed using this algorithm.
   2. Classification – It is used to find the accuracy in the classification process.

Several types of classification models are given below:

1. 1. Neural Networks
   2. Support Vector Machines (SVM)
   3. Classification by decision tree induction
   4. Bayesian Classification
   5. Classification Based on Associations

• Clustering Technique:
  Like classification, clustering is a data mining technique in which the data sets are grouped according to their similarities. It is an ancient technique in which segmentation is done and similar and dissimilar data segregates. For instance: a school can classify its students based on their classes, roll numbers, sections and grades. It is an essential technique to know the deep concepts of the data to be clustered.

Several different types of clustering methods are given below:

• 1. Partitioning Methods
  2. Density-Based Methods
  3. Grid-Based Methods
  4. Hierarchical Agglomerative methods
  5. Model-Based Methods
• Association Technique:
  Association is an important data mining technique used to find an association between different datasets. The term is related to diverse tracking patterns and is helpful in creating groups of data that have dependently linked variables. You may have checked several shopping sites having a column of people also checked/ visited/bought products. This highlights the implementation of association technique.

This technique mainly focusses on 2 types of information:

1. Support – When or in what frequency is association applied?
2. Confidence – How many times is the similarity process accurate?

There are 2 core steps in this technique:

1. Finding the frequently occurring data sets without missing any.
2. Creating strong association rules from the given frequent data sets.

All the different types of association techniques are given below:

1. Multilevel Association
2. Multidimensional Association
3. Quantitative Association

These techniques find useful applications in the retail industry where different patterns are identified to analyze the audience interest and how businesses should focus to boost up their profits.

• Prediction Technique:
  This technique is responsible for the complete understanding of how the future trends will transform depending upon a predictor variable. This data mining technique tracks the entire historical changes that have taken place and helps in providing useful data for future estimation. This is essential for making necessary changes in the previous data so that future estimate can be a precise one. For instance: when you track the performance of all the product sales to predict a future graph that would lead to the growth and development of the company.

Some common prediction techniques:

1. Multiple Linear Regression
2. k-Nearest Neighbors
3. Neural Network
4. Regression tree

Although there are many data mining techniques as well such as visualization, induction decision tree techniques, neural networks etc., here we have tried to cover the famous ones, which have gained popularity over the time owing to their best decision making and high value of reporting. These techniques help in analyzing a large amount of raw data and provide relevant solutions to develop predictive models and patterns. This is helpful in making smart business decisions and solves some of the major problems that they face in their businesses.