I've been looking for this aswer about a few months, and nothing! Researching on it, I believe that both are same. But, with only one markable difference: clustering is a type of unsupervised learning, and classification is a type of supervised learning. I believe that it is the only difference, and, of course, this dictates the way that the algorithm starts. But the results are essentially similar: grouped data.
Good luck in your question. I hope I've helped!
There is only a slight difference between discrimination and classification in data mining. Discrimination can be negative and classification is generally just factual.
Clustering algorithms may be classified as listed below:Exclusive ClusteringOverlapping ClusteringHierarchical ClusteringProbabilistic Clustering
Both of them utilize expectation-maximization strategy to converge to a minimum error condition. While K-Medoids require the cluster centters to be centroids, in k-Means the centers could be anywhere in the sample space. k-Medoids is more robust to outliners than k-Means therefore results in more quality clustering. It is also computationally more complex.
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Classification is a type of supervised learning (Background knowledge is known) and Clustering is a type of unsupervised learning(No such knowledge is known).
what are the difference between clustering and cross enrollment
image segmentation refers to clustering or grouping of homogeneous pixels into various groups while classification is next hierarchy which labell those clustered pixels as different classes..
see zyxo.wordpress.com/2010/07/17/the-difference-between-segmentation-and-clustering/ for a neat explanation.
difference between knowledge classification and book classification?
There is only a slight difference between discrimination and classification in data mining. Discrimination can be negative and classification is generally just factual.
Database is defines as a collection related records/data. When the data in the database is grouped based on some classification it is called database clustering.
data classification in statistics
Clustering is a group of resources trying to achieve the same objective, whereas load balancing is having several different servers that are completely unaware of each other and trying to achieve the same objective.
Modern classification is based on evolutionary relationships between organisms while traditional classification is not.
Classification is sorting out things due to scientific process. Partition is eminent domain.
so scientists can tell the difference between animals