Difference Between Classification vs. Clustering

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Main Difference

The key difference between classification and clustering is that classification is used in supervised learning technique whereas clustering is used in unsupervised leaning.

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Classification vs. Clustering

Learning is one of the most important aspects of education. There are methods of learning, and two main types of learning are classification and clustering. These are thought to be the same, but there is a lot of difference between classification and clustering. If we talk about the main difference, then the main difference between classification and clustering is that classification is used in supervised learning technique whereas clustering is used in unsupervised leaning. In classification, predefined labels are assigned to instances by properties whereas in clustering instances are instances are grouped based on their features and properties. There is a difference between supervised learning and unsupervised learning as in supervised learning training is provided to system and class label of training is tested whereas in unsupervised learning there is no training and learning.

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The process of learning a model that elucidate different predetermined classed of data is known as classification. There are basically two steps one is learning process and other is classification. In first step that is learning first a classification model is made and this classification model is used to prefigure the class labels of the data. Supervised learning is used in classification as in ban customer who has applied for a loan has to check that he is not risky and what is his or her salary. The constructed data is used to classify new data. When a group of data is organized into classes and clusters under a technique, then this technique is known as clustering. The objects that are inside the cluster will have the same attributes. The objects of two different clusters will have different attributes. There is a disjoint relationship between two clusters. The main purpose of clustering is to divide the whole data into multiple clusters. Class labels of objects are not known before and clustering the pertains to unsupervised learning. Similarity function in clustering is the similarity between two objects where the distance between those two objects is measured. There are two types of clusters that are mammal and reptile. Cluster of the mammal includes human, leopard, elephant etc. on the other hand reptile clusters includes snakes, lizard, komodo and may more. There is a training set of data that is used as a learning step in classification. There is a record in training data that is associated with an attribute that is known as a class label. The class label is an attribute that is associated with clustering. There is a decision tree that is a graph that forms a decision tree in a set of rules. The graphical depiction of interpretation of each class is known as a decision tree. A special application of classification rules is known as regression. In the place of mapping, a tuple of data from a relation useful value of a variable is predicted. The common classification algorithm is decision tree, neural networks, logistic regression etc.

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Comparison Chart

BasisClassificationClustering
MeaningClassification is used in supervised learning techniqueClustering is used in unsupervised leaning.
LearningClassification is supervised learningClustering is unsupervised learning
TrainingTraining is not provided in the classificationTraining is provided in clustering

What is Classification?

The process of learning a model that elucidate different predetermined classed of data is known as classification. There are basically two steps one is learning the process, and other is classification. In the first step that is learning first a classification model is made, and this classification model is used to prefigure the class labels of the data. Supervised learning is used in classification as in ban customer who has applied for a loan has to check that he is not risky and what is his or her salary. The constructed data is used to classify new data. There is a training set of data that is used as a learning step in classification. There is a record in training data that is associated with an attribute that is known as a class label. The class label is an attribute that is associated with clustering. There is a decision tree that is a graph that forms a decision tree in a set of rules. The graphical depiction of interpretation of each class is known as a decision tree. A special application of classification rules is known as regression. In the place of mapping, a tuple of data from a relation useful value of a variable is predicted. The common classification algorithm is decision tree, neural networks, logistic regression etc.

What is clustering?

When a group of data is organized into classes and clusters under a technique, then this technique is known as clustering. The objects that are inside the cluster will have the same attributes. The objects of two different clusters will have different attributes. There is a disjoint relationship between two clusters. The main purpose of clustering is to divide the whole data into multiple clusters. Class labels of objects are not known before and clustering the pertains to unsupervised learning. Similarity function in clustering is the similarity between two objects where the distance between those two objects is measured. There are two types of clusters that are mammal and reptile. A cluster of the mammal includes human, leopard, elephant etc. on the other hand reptile clusters includes snakes, lizard, komodo and may more.

Examples

  • Family Agamidae – agamas
  • Family Chamaeleonidae – chameleons
  • Family Iguanidae
  • Subfamily Corytophaninae – casquehead lizard
  • Subfamily Iguaninae – iguanas
  • Subfamily Leiocephalinae
  • Subfamily Leiosaurinae
  • Subfamily Liolaeminae
  • Subfamily Oplurinae – Madagascar iguanids
  • Family Crotaphytidae – collared and leopard lizards
  • Family Phrynosomatidae – horned lizards
  • Family Polychrotidae – anoles
  • Family Hoplocercidae – wood lizards
  • Family Tropiduridae – Neotropical ground lizards
  • Family Gekkonidae – geckos
  • Family Pygopodidae – legless lizards
  • Family Dibamidae – blind lizards
  • Family Cordylidae – spinytail Lizards
  • Family Gerrhosauridae – plated lizards
  • Family Gymnophthalmidae – spectacled lizards
  • Family Teiidae – whiptails and tegus
  • Family Lacertidae – Lacertids
  • Family Scincidae – skinks
  • Family Xantusiidae – night lizards
  • Family Anguidae – glass lizards
  • Family Anniellidae – American legless lizards
  • Family Xenosauridae – knob-scaled lizards
  • Family Helodermatidae – gila monsters
  • Family Lanthanotidae – earless Monitor lizards

Mammals

  • Family Tenrecidae – tenrecs and otter shrews
  • Subfamily Geogalinae
  • Genus Geogale – long-eared tenrec
  • Subfamily Tenrecinae
  • Genus Setifer – greater hedgehog tenrec
  • Genus Echinops – lesser hedgehog tenrec
  • Genus Hemicentetes – streaked tenrec
  • Genus Tenrec – common tenrec
  • Subfamily Oryzorictinae
  • Genus Microgale – shrew tenrecs
  • Genus Oryzorictes – rice tenrecs
  • Genus Limnogale – web-footed tenrec
  • Subfamily Potamogalinae
  • Genus Micropotamogale – otter shrews
  • Genus Potamogale – giant otter shrew
  • Family Chrysochloridae – golden moles
  • Genus Chrysospalax – giant golden moles
  • Genus Calcochloris – yellow golden moles
  • Genus Eremitalpa – Grant’s golden mole
  • Genus Cryptochloris – cryptic golden moles
  • Genus Chrysochloris – Cape golden moles
  • Genus Chlorotalpa – forty-toothed golden moles
  • Genus Carpitalpa – Arend’s golden mole
  • Genus Neamblysomus – lesser narrow-headed golden moles

Key Differences

  1. Classification is used in supervised learning technique whereas clustering is used in unsupervised leaning.
  2. Classification is supervised learning whereas clustering is unsupervised learning
  3. Training is not provided in classification whereas training is provided in clustering

Comparison Video