Good Old Towards Data Science. My favorite publication named after a thing which is not actually what its name would suggest. There must be deeper meaning in that somewhere. In any case I love you Towards Data Science so I will once again provide some free editing for one of your articles.
Unsupervised Dataset Analysis With Modern Computers Programmed Using an Object Oriented High Level Programming Language with Dynamic Semantics
Unsupervised Learning is a class of Machine Learning techniques that like machine learning is not learning, but is instead a form of modern computing that falls within the class of modern computing techniques referred to erroneously and illogically as machine learning. It is used to find the patterns in data. The data given to unsupervised algorithm are not labelled, which means only the input variables(X) are given with no corresponding output variables. In unsupervised learning, the algorithms behave exactly as they are programmed to and execute a search function which (if the search parameters are set appropriately) may find what we we refer to as interesting structures in the data, but what the computer defines as the outputs of the algorithms which make up the program that was run to analyze the given data set. Since no learning is happening we will correctly refer to such analyses as unsupervised and supervised data set analysis.
Supervised Vs Unsupervised Dataset Analysis
In supervised data set analysis, the system uses a data set that was previously programmed into it as the baseline data from which to calculate the outputs based on the rules of the algorithms that make up its programming. (On the other hand, in unsupervised learning, the system calculates the outputs following the rules of the algorithms that make up its programming but those rules do not reference a baseline data set.) So if the dataset is labelled (previously programmed into the computer and referenced as a baseline data set) it comes under a supervised problem, it the dataset is only composed of data entered at the time of the analysis of that data set without reference to any baseline data then it is an unsupervised problem.
The image to the left is an example of supervised data set analysis; we use regression techniques to find the best fit line between the features. While in unsupervised data set analysis the inputs are segregated based on features and the prediction is based on which cluster it belonged. As in all modern computing techniques mathematical and statistical approaches are used to find the solution to various queries we may make of the data set. In no case does the computer/machine learn anything as it is incapable of learning by the very definition of the words that make up the term. In fact the term itself is a logical contradiction and something which is logically impossible, a learning machine. If machine could learn it would no longer be a machine so it is puzzling as to why this particular form of modern computing is still referred to as such.