10 Difference Between Classification And Clustering In Data Mining

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Clustering and classification are the two main techniques of managing algorithms in data mining processes. Although both techniques have certain similarities such as dividing data into sets. The main difference between them is that classification uses predefined classes in which objects are assigned while clustering identifies similarities between objects and groups them in such a way that objects in the same group are more similar to each other than those in other group.

Classification and clustering help solve global issues such as crime, poverty and diseases through data science.

What Is Classification?

Classification is a classic data mining technique based on machine learning, typically, classification is used to classify each item in a set of data into one of a predefined set of classes or groups. The goal of classification is to accurately predict the target class for each case in data. For example, in banking industry, classification models are used to identify loan applicants as low, medium or high credit risks.

Types of classification algorithms in machine learning:

  • Neural Networks
  • Linear Classifiers: Logistic Regression, Naïve Bayes Classifier
  • Random Forest
  • Decision Trees
  • Nearest Neighbor
  • Boosted Trees

What You Need To Know About Classification

  1. Classification is a supervised learning approach in which the computer program learns from the data input given to it and then uses this learning to classify new observations.
  2. Classification is the result of supervised learning, which means that there is a known label that you want the system to generate. The machines learn from already labeled or classified data.
  3. Classification algorithm requires training data.
  4. With classification, the groups (or classes) are specified before hand, with each training data set belonging to a particular class.
  5. Classification algorithms are supposed to learn the association between the features of the instance and the class they belong to.
  6. Classification model is uses pre-defined instances.
  7. Classification is more complex when compared to clustering as there are many levels in classification phase.
  8. Classification generally consists of two stages, that is training (model learns from training data set) and testing (target class is predicted).
  9. Classification deals with both labeled and unlabeled data in its processes.
  10. Classification aims to determine the definite group a certain object belongs to.

Applications Of Classification Algorithm

  • Speech recognition
  • Handwriting recognition
  • Biometric identification
  • Document classification etc.

What Is Clustering?

Clustering is a Machine Learning technique that involves the grouping of data. Given a set of data, a clustering algorithm can be use to categorize each data into a specific group.  In theory, data that is in the same group should have similar properties or features while data in other different groups should have highly dissimilar properties or features.

Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.

Types of clustering algorithms in machine learning include:

  • K-means
  • Hierarchical clustering
  • DBSCAN
  • Fuzzy C-means
  • Gaussian (EM)

What You Need To Know About Clustering

  1. Clustering is a technique of organizing a group of data or objects into groups in such a way that objects in the same group are more similar to each other than those in other group.
  2. Clustering is the result of unsupervised learning where the input dataset is unlabeled.
  3. Clustering algorithm does not require training data.
  4. With clustering, the groups (or clusters) are based on the similarities of data instances to each other.
  5. No predefined output class is used in training and the clustering algorithm is supposed to learn the grouping.
  6. Clustering does not assign pre-defined label to each and every group.
  7. Clustering is less complex when compared to classification because its only grouping that it’s done under clustering.
  8. Clustering is generally made up of a single phase that is (Grouping).
  9. Clustering deals with unlabeled data.
  10. The main objective of clustering is to narrow down relationships as well as learn novel information from hidden patterns.

Application Of Clustering

  • It can be used in Customer Segmentation whereby customers are placed into groups or segments such that each customer segment consists of customer with similar market characteristics i.e spending behavior, average transaction value, total number of transactions.
  • It can be used in social network analysis; examples are generating sequences in images, videos or audio.
  • Clustering can also be used for trend detection in dynamic data by making various clusters of similar trends.
  • Clustering is also used in cloud computing environments, whereby clustered storage increases reliability, performance, manages transfer of workloads between servers and provides access to all files from any server regardless of the physical location of the data.

Difference Between Classification And Clustering In Tabular Form

BASIS OF COMPARISON CLASSIFICATION CLUSTERING
Description Classification is a supervised learning approach in which the computer program learns from the data input given to it and then uses this learning to classify new observations.   Clustering is a technique of organizing a group of data or objects into groups in such a way that objects in the same group are more similar to each other than those in other group.  
Classification is the result of supervised learning, which means that there is a known label that you want the system to generate. The machines learn from already labeled or classified data.   Clustering is the result of unsupervised learning where the input dataset is unlabeled.  
Training Data Classification algorithm requires training data.   Clustering algorithm does not require training data.  
Basis With classification, the groups (or classes) are specified before hand, with each training data set belonging to a particular class.   With clustering, the groups (or clusters) are based on the similarities of data instances to each other.  
Predefined Out Classification algorithms are supposed to learn the association between the features of the instance and the class they belong to.   No predefined output class is used in training and the clustering algorithm is supposed to learn the grouping.  
Complexity Classification is more complex when compared to clustering as there are many levels in classification phase.   Clustering is less complex when compared to classification because its only grouping that it’s done under clustering.  
Stages/Phases Classification generally consists of two stages, that is training (model learns from training data set) and testing (target class is predicted).   Clustering is generally made up of a single phase that is (Grouping).  
Labeling Classification deals with both labeled and unlabeled data in its processes.   Clustering deals with unlabeled data.  
Objective Classification aims to determine the definite group a certain object belongs to.   The main objective of clustering is to narrow down relationships as well as learn novel information from hidden patterns.  

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