The difference between classification and clustering is that classification is "supervised" while clustering is "unsupervised" learning technique. The aim of the classification is to use data about objects with existing assignments to classes to create a mathematical model that can predict a correct class of an unknown object. On the other hand, clustering defines new classes of objects based on the similarity of objects' features. A hybrid approach may comprise a classification model that predicts classes that were defined with a clustering.

of course we can make hybrid by using clustering and classification methods. even some method using for clustering and classification ,for example neural network is used for clustering and classification and i use it.

The difference between classification and clustering is that classification is "supervised" while clustering is "unsupervised" learning technique. The aim of the classification is to use data about objects with existing assignments to classes to create a mathematical model that can predict a correct class of an unknown object. On the other hand, clustering defines new classes of objects based on the similarity of objects' features. A hybrid approach may comprise a classification model that predicts classes that were defined with a clustering.

Thank you, dear Mohammed Deeb, for your answer.

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I agreed that the extraction of new features from a given dataset and then using these new features in classification. the Cluster mostly will be unsupervised and classification will be supervised classification. We can perform classification on the basis of clustering. We also can combine these two approaches.

yes, you can by using clustering to extract unseen features from the dataset then using classification or regression to learn and solve problems, Besides you can use a hybrid solution.

Nada M. Al Hakkak classification means in short "separating" based on attributes where as clustering means "grouping" based on characteristics. still now i didn't see nowhere both get combine even there is no such a algorithm for that, but both are suppotive or respective to each other in machine learning.

Another way of the use of a hybrid model is when the regression model gives poor accuracy. Then, it is possible to cluster the output (supervised learning) with very high accuracy and creating separate regression models within each class. In fact, the mechanics of regression is the same as for clustering. If we divide the full range of outputs onto equal sections, we are searching for the fitting to the middle points of classes surrounded by r radius. Lowering r (multiplying number of classes) we are coming to regression (then r=0).

If we know the label of the dataset, then we can use the classification techniques to predict the class in which the data fall. So that the classification techniques are called supervised. Labelling the data is said to be classification.

If we do not have any idea about the label or class of the dataset, that is, just a group of heterogeneous datasets, at that situation we must use the clustering techniques to cluster the dataset into various groups/clusters. Thus, the clustering process of the datasets is said to be unsupervised.

I think, best of my knowledge, such a situation does not arise to combine both the techniques. Because the classification and clustering processes are used for different purposes.

Below, a paper describing a real-world application that can be solved combining classification and regression:

Article Combining classification and regression for improving permea...

Classification is a solution to the problem of identification (recognition, diagnostics). This is a comparison of a specific object with generic class images. And clustering is the comparison of the generalized images of the classes with each other. In a special case, if generalized class images are created each based on a single example, i.e. specific objects, then clustering is equivalent to classification by the k-nearest neighbor method. If there is no class that a particular object resembles above a certain threshold during recognition, then a generalized image can be created based on it and added to the model.