You may wonder how to find an underlying structure of a dataset. You may also wonder about summarizing it along with grouping it to make it useful. Do you wonder about representing the data in a compressed format effectively? You should rest assured that these have been essential goals of unsupervised learning. It has been deemed unsupervised due to you starting with labeled data.
It would be pertinent to mention here that two major unsupervised tasks would be a clustering of the data into various groups for being similar. The other essential task would be the reduction of dimensionality of compressing the data along with maintaining the usefulness along with the structure.
Opposite to supervised training, it would not be easier to come up with various kinds of metrics for how the unsupervised learning algorithm has been performing. You should rest assured that performance would be specific and subjective.
Working of an unsupervised algorithm
It would not be wrong to state that the unsupervised algorithm works with unlabeled data. The major purpose would be exploration. When supervised machine learning works with defined rules, the unsupervised machine learning would be working under conditions where the results would not be known. It would be defined in the process.
If you wonder where unsupervised machine learning is used for, find it here.
- Exploring the structure of the information along with detecting the unique patterns
- Extraction of useful insights
- Implementation of the gathered data in the operation for increasing the overall efficiency of the process of making the correct decision
To make it happen, you should rest assured that unsupervised learning would need two major techniques, inclusive of clustering and dimensionality reduction.
These two essential techniques would help you in the best manner possible. Clustering would entail an exploration of the data whereas; dimensionality reduction would entail the process of distilling the relevant information.