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Types of clustering algorithm

Updated: 12/10/2022
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Clustering algorithms may be classified as listed below:

  • Exclusive Clustering
  • Overlapping Clustering
  • Hierarchical Clustering
  • Probabilistic Clustering
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When is minimum mean square error algorithm used?

This type of algorithm is commonly used in n dimensional clustering applications. This mean is commonly the simplest to use and a typical algorithm employing the minimum square error algorithm can be found in McQueen 1967.


What types of data used in algorithm?

Whatever data you need. If you need the algorithm to operate with many different types of data, and you are programming in C++, you could use generic programming practices and use templates.


What is the difference between clustering and classification?

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!


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Difference between K-mean and K-medoids algorithm for clustering techniques in data mining?

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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When is minimum mean square error algorithm used?

This type of algorithm is commonly used in n dimensional clustering applications. This mean is commonly the simplest to use and a typical algorithm employing the minimum square error algorithm can be found in McQueen 1967.


How is KNN different from k-means clustering?

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.


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What is the definition of k-means in data mining?

K-means clustering is a data mining learning algorithm used to cluster observations into groups of related observation without any prior knowledge of those relationships.


What types of data used in algorithm?

Whatever data you need. If you need the algorithm to operate with many different types of data, and you are programming in C++, you could use generic programming practices and use templates.


How many different ways can SQL clustering be used?

"There is two types of SQL clustering, load-balancing clusters and failover clusters." "There are two types, and which one you use depends on what you need the cluster for. One cluster (load-balancing) is used to spread out the server load while the other (failover) is used more as a security measure."