Classification in Data Mining: Types of Classifiers

Data mining efficiently makes finding hidden patterns in large amounts of data possible. Potentially, these complex patterns can be used to understand and forecast future behaviour or make informed decisions about a business.

Although raw data is a precious resource, it is only usable when refined and processed. Using data classification techniques, you can properly classify and utilise data to its fullest extent.

What is classification in data mining?

Classification is an organisational method used in data mining to divide data points into several categories. The process of data classification is frequently carried out with the help of machine learning techniques that utilise artificial intelligence (AI).

Machine learning and current classification methods are closely related. AI can reorganise the variables and elements of a data collection into predefined classes or groupings. Advanced data categorisation AI solutions can support the data mining process with the use of:

• Statistics

• Decision trees 

• Linear and logistic regression

• Neural nets

• Various other methods

Types of classification methods

You'll first need to understand the two available classification approaches before you explore the types of classification algorithms used in data mining. We can broadly categorise the classification methods into two groups:

Generative

An algorithm for generative classification generates the distribution of unique classes. It tries to understand the model that generates the data by estimating the distributions and model assumptions. Generative algorithms are typically used to forecast previously unknown data.

Examples of generative algorithms are the Gaussian mixture model, hidden Markov model, and Naive Bayes classifier. 

Discriminative

A simple classification technique determines a class for a row of data. Instead of relying on the data's distributions, the model depends on the quality of the observed data.

Examples of discriminative algorithms are k-nearest neighbour (k-NN), support vector machine (SVM), random forest, and artificial neural network (ANN).

Types of classifiers in machine learning

There are many types of classifications in data mining used in machine learning. Some of the popular ones are outlined below:

Logistic regression

Since logistic regression only considers binary outcomes, the results are fairly straightforward. This algorithm can interpret the data as true or false, positive or negative, passed or failed, or any alternative outcome with just two logical outcomes. It helps predict a categorical dependent variable from the independent factors’ set. 

Since the technique uses a binary framework, it can handle any true or false statement. As a result, a logistic regression technique can be efficiently used for tasks like object recognition, which allows image recognition software to identify objects in pictures by comparing them to their collection of examples. 

Naive Bayes

This algorithm determines the probability that a particular piece of data belongs to a specific category and then classifies the data by that probability. Using Naive Bayes includes sorting through enormous amounts of data to locate particular pieces relevant to a topic.

Linear regression

Linear regression uses supervised learning to execute regression. It employs independent variables to model a prediction value. Using this method, it is possible to analyse the relationship between certain variables and make forecasts based on the results of this analysis. It forecasts the value of the dependent variable according to a particular independent variable by identifying the linear relation between the two variables. 

K-nearest neighbours

This classification in data mining uses associations to categorise data based on how close together they are. Using proximity as a classifier, you can predict how a single data point will be classified. K-nearest neighbours are helpful when you're dealing with massive amounts of data.

Support vector machines

The support vector machine (SVM) classifier separates data by drawing a line between two classes, essentially dividing them into two categories based on similarities and differences. The newly added data points categories are predicted based on the side of the line these fall into once mapped within the same area. SVMs are advantageous in high-dimensional spaces, memory efficient, and versatile.

Random forest

This type of classification uses the results of averaging a large group of decision trees to group them into various data subsets. The sub-sample value is always identical to the input sample size. This approach uses the bagging method to produce a forest from decision trees’ collection. The bagging approach combines several learning models and enhances the final result. 

Types of classifiers in deep learning

Application of these deep learning classifications occurs when there is a large amount of data and many distinct features. They can extract relationships with more complicated structures than statistical learning methods. 

Artificial neural networks

This type of classification in data mining is like a dense network of logistic regressions linked together by control gates. The artificial neural network can be deepened or expanded to meet different needs. To find the best prediction model, it is usual practice to experiment with the depth and breadth of the network. For a binomial classification, the final result is typically a probability value. Expansion can occur up to multilabel or multiclass data classification. 

Convolutional neural networks

Convolutions are a method of compressing the feature set's dimensions. They are frequently applied to pictures where the basic feature set consists of almost every pixel. They can take in an input image, assign weights and biases to different aspects of the image, and differentiate one from the other. 

Recurrent neural networks

RNN-based classifiers work well in natural language processing, where the placement and order of words are crucial for data classification in data mining. They also find applications in the analysis of stock data and audio processing. They are a part of popular applications like Siri, voice search, and Google Translate.

When there is a sequential data or time series component in the data, recurrent neural networks and their modifications perform well. 

These are suitable for ordinal or temporal problems, including language translation, speech recognition, natural language processing, and image captioning. 

Next steps

Data mining is a broad field that examines massive amounts of data. It combines techniques from several disciplines, including artificial intelligence, machine learning, statistics, and pattern recognition. To learn more, consider a comprehensive online course such as the Data Mining Specialisation from the University of Illinois on Coursera.