What are the characteristics of data?
Compare the difference in each of the following clustering types: prototype-based, density-based, graph-based.
- What is a scalable clustering algorithm?
- How do you choose the right algorithm?
Reply:
The Cluster Analysis is regularly essential for the succession of analysis of factor analysis, group analysis, and at last, discriminant analysis. Initial, a factor analysis that diminishes the measurements and consequently the quantity of factors makes it simpler to run the cluster analysis. Likewise, the factor analysis limits multi-collinearity impacts. The following analysis is the cluster analysis, which distinguishes the gathering. Finally, a discriminant analysis checks the integrity of attack of the model that the cluster analysis found and profiles the groups(SM)
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There are some important factors to consider for choosing the right algorithms. It is always good practice to gather a good amount of data to get reliable predictions. In real-world applications, algorithms require more time to train on extensive training data to achieve high accuracy. Many algorithms work on the assumption that a straight line can separate classes. This aspect is called linearity, which can be found out by logical regression or SVM.(AD)
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