Classification in Data Mining – A Beginner’s Guide in 4 Easy Points

Introduction

Classification in data mining is definitely an expanding field of study. Classification plays an integral role in the context of mining techniques. As suggested by its name, this is a process where you classify data. And, many decisions need to be made to bring the data together. Often, it depends on a set of input variables. The classification depends on a series of acknowledgements and data instances. Over the years, “classification techniques in data mining”, “classification algorithms in data mining” and “rule-based classification in data mining” have turned into trending topics. In this post, you will read about the most important, and common theories around classification in data mining.

1. What is Classification?
2. Types
3. Example
4. Applications

1) What is Classification?

Before you understand the different types of classification in data mining, you need to define classification in data mining. Data classification in data mining is a common technique that helps in organizing data sets that are both complicated and large. This technique often involves the use of algorithms that can be easily adapted to improve the quality of data. This is why supervised learning is strongly associated with the classification process in data mining. The ultimate objective of classification is to relate a variable of interest with observed variables. The actual variable of interest is meant to be of “Qualitative” type.

The link is established for the purpose of “Prediction”. The algorithm required for performing the classification is known as the classifier. And, the observations made are known as instances. The classification model in data mining is used when the variable of focus is more “Qualitative”. There are several different types of classification. And, each of these algorithms is used to extract useful information from the dataset. Organizations invest in these algorithms to master more about the preferences and behaviour of their clients.

2) Types

Here are seven important types of classification algorithms. One, structured data classification is often performed on unstructured and structured data. This technique is commonly used when data has to be categorized carefully into several classes. The ultimate aim of this technique is to identify classes and categorize where new datasets can be placed. Common names heard as a part of structured data classification would be a feature, classifier, classification model and binary classification.

Logistic Regression is a commonly used machine learning algorithm. This is used for the classification of complex datasets. During the application of this algorithm, probabilities are taken into consideration. The probability of a possible outcome, from a specific trial, is evaluated and modelled using a logistic function. This algorithm is extremely effective and is designed for a purpose. It helps in understanding variables that can influence the outcome of a specific incident. However, the predictions happen only in binaries. This means the predictors need to be independent of one another.

Naive Bayes would be the next classification algorithm. As suggested by its name, it depends on the Bayes theorem. It focuses on the assumption of a pair of independent features. The Naive Bayes method works perfectly in real-world scenarios. For instance, spam filtering and document classification are two places where Naive Bayes is used exhaustively. What makes this algorithm special would be the need for minimal information or training data. This gets rid of many extra parameters. Also, this is one of the fastest algorithms around.

Stochastic Gradient Descent is an efficient and simple approach to suit the needs of linear data models. This algorithm is extremely useful when you have large samples. The algorithm is configured to handle loss functions. It also features penalties for classification in data mining. This algorithm is a little tricky because it has multiple hyper-parameters. And, the logic is extremely sensitive to concepts like feature scaling.

K-Nearest Neighbours is known as a lazy form of learning. It does not focus on internal models. However, it does store multiple instances of data for training purposes. The classification algorithm is calculated using a simple voting system, which focuses on the k-nearest neighbours to every coordinate on the system. The algorithm is extremely robust and is easy when it comes to noise training. Moreover, it is useful when there are huge volumes of data involved.

Classification algorithms remain incomplete without the decision trees. These are algorithms that focus on data attributes, which are brought together with classes. The decision tree generates a sequence of rules. These rules are used for the classification. When compared to many other methods, decision trees are easy to visualize and understand. It does not require plenty of data preparation too. Both categorical and numerical data can be handled using the decision trees.

Decision trees often expand to form random forests. These are classifiers with meta estimators, which can fit multiple decision trees into one. The role of the random forest is to improve the accuracy of the controls and models used to make predictions.

3) Example

Here is a simple example of the classification algorithm for data mining. Given a collection of “n” attributes which can be both categorical or ordinal. And, you are provided with a set of “K” classes. The collection is supported with a set of training instances, each carefully labelled. The problem statement expects the algorithms to determine a classification rule, which can be used to predict the class of each instance. The prediction needs to make use of the values, in each attribute too. This is a simple generalization of the entire concept of learning. Mainly because it involves only two classes. In a real-time scenario, there will be more than two such classes.

4) Applications

Classification problems are present in every industry. For example, email spam is a great example to demonstrate the need for classification in data mining. The goal of classification algorithms in data mining in this application is to understand if an email is a spam or not. It helps in deciding if an email has to be redirected to the junk folder. Another application for classification in data mining would be to recognize handwritten digits. The goal of this use case is to spot digits that are between 0 and 9 successfully. Another use of classification would be image segmentation. This is much more complicated than any other application of this technology. 

Conclusion

Hope this article played a role in helping you better understand the classification in Data Mining. 

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