Classification in Data Mining Explained: Types, Classifiers & Applications [2021]

Data mining is one of the most important parts of data science. It allows you to get the necessary data and generate actionable insights from the same to perform the analysis processes. 

In the following column, we’ll cover the classification of data mining systems and discuss the different classification techniques used in the process. You’d learn how they are used in today’s context and how you can become an expert in this field. 

What is Data Mining?

Data mining refers to digging into or mining the data in different ways to identify patterns and get more insights into them. It involves analyzing the discovered patterns to see how they can be used effectively. 

In data mining, you sort large data sets, find the required patterns and establish relationships to perform data analysis. It’s one of the pivotal steps in data analytics, and without it, you can’t complete a data analysis process. 

Data mining is among the initial steps in any data analysis process. Hence, it’s vital to perform data mining properly. 

What is Classification in Data Mining?

Classification in data mining is a common technique that separates data points into different classes. It allows you to organize data sets of all sorts, including complex and large datasets as well as small and simple ones. 

It primarily involves using algorithms that you can easily modify to improve the data quality. This is a big reason why supervised learning is particularly common with classification in techniques in data mining. The primary goal of classification is to connect a variable of interest with the required variables. The variable of interest should be of qualitative type. 

The algorithm establishes the link between the variables for prediction. The algorithm you use for classification in data mining is called the classifier, and observations you make through the same are called the instances. You use classification techniques in data mining when you have to work with qualitative variables. 

There are multiple types of classification algorithms, each with its unique functionality and application. All of those algorithms are used to extract data from a dataset. Which application you use for a particular task depends on the goal of the task and the kind of data you need to extract. 

Types of Classification Techniques in Data Mining

Before we discuss the various classification algorithms in data mining, let’s first look at the type of classification techniques available. Primarily, we can divide the classification algorithms into two categories:

  1. Generative
  2. Discriminative

Here’s a brief explanation of these two categories:

Generative

A generative classification algorithm models the distribution of individual classes. It tries to learn the model which creates the data through estimation of distributions and assumptions of the model. You can use generative algorithms to predict unseen data. 

A prominent generative algorithm is the Naive Bayes Classifier. 

Discriminative

It’s a rudimentary classification algorithm that determines a class for a row of data. It models by using the observed data and depends on the data quality instead of its distributions. 

Logistic regression is an excellent type of discriminative classifiers.

Classifiers in Machine Learning

Classification is a highly popular aspect of data mining. As a result, machine learning has many classifiers:

  1. Logistic regression
  2. Linear regression
  3. Decision trees
  4. Random forest
  5. Naive Bayes
  6. Support Vector Machines
  7. K-nearest neighbours

1. Logistic Regression

Logistic regression allows you to model the probability of a particular event or class. It uses a logistic to model a binary dependent variable. It gives you the probabilities of a single trial. Because logistic regression was built for classification and helps you understand the impact of multiple independent variables on a single outcome variable. 

The issue with logistic regression is that it only works when your predicted variable is binary, and all the predictors are independent. Also, it assumes that the data doesn’t have any missing values, which can be quite an issue. 

2. Linear Regression

Linear regression is based on supervised learning and performs regression. It models a prediction value according to independent variables. Primarily, we use it to find out the relationship between the forecasting and the variables. 

It predicts a dependent variable value according to a specific independent variable. Particularly, it finds the linear relationship between the independent variable and the dependent variable. It’s excellent for data you can separate linear and is highly efficient. However, it is prone to overfitting and nose. Moreover, it relies on the assumption that the independent and dependent variables are related linearly. 

3. Decision Trees

The decision tree is the most robust classification technique in data mining. It is a flowchart similar to a tree structure. Here, every internal node refers to a test on a condition, and each branch stands for an outcome of the test (whether it’s true or false). Every leaf node in a decision tree holds a class label. 

You can split the data into different classes according to the decision tree. It would predict which classes a new data point would belong to according to the created decision tree. Its prediction boundaries are vertical and horizontal lines. 

4. Random forest

The random forest classifier fits multiple decision trees on different dataset sub-samples. It uses the average to enhance its predictive accuracy and manage overfitting. The sub-sample size is always equal to the input sample size; however, the samples are drawn with replacement. 

A peculiar advantage of the random forest classifier is it reduces overfitting. Moreover, this classifier has significantly more accuracy than decision trees. However, it is a lot slower algorithm for real-time prediction and is a highly complicated algorithm, hence, very challenging to implement effectively. 

5. Naive Bayes

The Naive Bayes algorithm assumes that every feature is independent of each other and that all the features contribute equally to the outcome. 

Another assumption this algorithm relies upon is that all features have equal importance. It has many applications in today’s world, such as spam filtering and classifying documents. Naive Bayes only requires a small quantity of training data for the estimation of the required parameters. Moreover, a Naive Bayes classifier is significantly faster than other sophisticated and advanced classifiers. 

However, the Naive Bayes classifier is notorious for being poor at estimation because it assumes all features are of equal importance, which is not true in most real-world scenarios. 

6. Support Vector Machine

The Support vector machine algorithm, also known as SVM, represents the training data in space differentiated into categories by large gaps. New data points are then mapped into the same space, and their categories are predicted according to the side of the gap they fall into. This algorithm is especially useful in high dimensional spaces and is quite memory efficient because it only employs a subset of training points in its decision function.

This algorithm lags in providing probability estimations. You’d need to calculate them through five-fold cross-validation, which is highly expensive. 

7. K-Nearest Neighbours

The k-nearest neighbor algorithm has non-linear prediction boundaries as it’s a non-linear classifier. It predicts the class of a new test data point by finding its k nearest neighbours’ class. You’d select the k nearest neighbours of a test data point by using the Euclidean distance. In the k nearest neighbours, you’d have to count the number of data points present in different categories, and you’d assign the new data point to the category with the most neighbors. 

It’s quite an expensive algorithm as finding the value of k takes a lot of resources. Moreover, it also has to calculate the distance of every instance to every training sample, which further enhances its computing cost. 

Applications of Classification of Data Mining Systems

There are many examples of how we use classification algorithms in our day-to-day lives. The following are the most common ones: 

  • Marketers use classification algorithms for audience segmentation. They classify their target audiences into different categories by using these algorithms to devise more accurate and effective marketing strategies. 
  • Meteorologists use these algorithms to predict the weather conditions according to various parameters such as humidity, temperature, etc. 
  • Public health experts use classifiers for predicting the risk of various diseases and create strategies to mitigate their spread. 
  • Financial institutions use classification algorithms to find defaulters to determine whose cards and loans they should approve. It also helps them in detecting fraud. 

Conclusion 

Classification is among the most popular sections of data mining. As you can see, it has a ton of applications in our daily lives. If you’re interested in learning more about classification and data mining, we recommend checking out our Executive PG Program in Data Science.

It’s a 12-month online course with over 300+ hiring partners. The program offers dedicated career assistance, personalized student support, and six different specialisations: 

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What is the difference between linear regression and logistic regression?

The following illustrates the difference between linear and logistic regression
Linear Regression -
1. Linear regression is a regression model.
2. A linear relationship between dependent and independent articles is required.
3. The threshold value is not added.
4. Root mean square Error or RMSE is used to predict the next value.
5. Gaussian distribution of the variable is assumed by linear regression.
Logistic Regression -
1. Logistic regression is a classification model.
2. The linear relationship between dependent and independent articles is not required.
3. The threshold value is added.
4. Precision is used to predict the next value.
5. The binomial distribution of the variable is assumed by the logistic regression.

What are the skills required to master data mining?

Data mining is one of the hottest fields of this decade and is in high demand. But to master data mining, there are certain skills that you must master. The following skills are a must to learn data mining.
a. Programming skills
The first and the most crucial step is to learn a programming language. There are still doubts about which language is the best for data mining but there are some preferable languages such as Python, R, and MATLAB.
b. The big data processing framework
Frameworks like Hadoop, Storm and Split are some of the most popular big data processing frameworks.
c. Operating System
Linux is the most popular and preferable operating system for data mining.
d. Database Management System
Knowledge of DBMS is a must to store your processed data. MongoDB, CouchDB, Redis, and Dynamo are some popular DBMS.

What is the importance of Classification in Data Mining?

The classification technique helps businesses in the following way:
The classification of data helps the organizations to categorize the huge amount of data to target categories. This enables them to identify areas with potential risks or profit by providing a better insight into the data.
For example, the loan applications of a bank. With the help of the classification technique, the data can be categorized into different categories according to credit risks.
The analysis is based on several patterns that are found in the data. These patterns help to sort the data into different groups.

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