How Unsupervised Machine Learning could be used for our benefit?
Though there are many machine learning and deep learning models created in the field of data science, there still remains an area that would be on interest to us which is known as unsupervised machine learning. Since there is abundance of data in various formats, it is easy to come to a conclusion that data is vast, and it can be used for machine learning purposes. However, it is important to note that though there is data abundance, it is not possible to use this data. This is because the data is not annotated. What this means is that time and effort must be spent on the part of humans to annotate the labels which could be used for machine learning purposes respectively.
Though there are a lot of machine learning algorithms, it is important to note that those algorithms only work when the data is supervised. What this means is that there needs to be annotation for the output values. Since a large portion of applications in the field of machine learning is supervised, there is often not a lot of emphasis placed on unsupervised learning.
Data is available everywhere. However, it is unstructured and not annotated. This means that though the data is available, it is not available in the form that is used for machine learning purposes. However, unsupervised learning could be used to segment and cluster the data so that interesting trends are found.
What is Unsupervised Machine Learning?
In unsupervised machine learning, there are no pre-defined output labels and there is only the presence of training input. Therefore, our models must be able to discover patterns and hidden insights without any supervision. There are various unsupervised machine learning algorithms that we would be going forward to understand and learn. Let us now go over some of the unsupervised machine learning models respectively.
K-means Clustering
As the name suggests, it is dividing the overall training data points into K clusters. It is an unsupervised machine learning technique where there is no requirement to have the output labels or dependent variable. In this method depending on the value of K, there would be those many centroids and they would constantly be changing until they reach a fixed point. Depending on the number of iterations and the clusters selected, the algorithm would be classifying the data points. It would first start with a single data point and later, it would form initial clusters and take an average of those clusters to find the centroid. This process continues until we get the desired output respectively. This method is also known as the ward’s method.
Association Rules
In this type of unsupervised learning, there are various rules that are taken into account when performing the clustering. In this method, the data is being associated based on a certain set of rules into different clusters and classified accordingly. Hence a large data is divided into smaller ones and into clusters based on the method of association.
Autoencoders
When we are dealing with huge datasets in machine learning, it would be computationally expensive for the models to perform well on the data. Therefore, they might require additional resources such as the GPU or the CPU for their effective functioning. Autoencoders reduce the dimensionality of data without losing data integrity. All of this ensures that the resources that are allotted for training the machine learning models would reduce to a large extent.
What are the applications of Unsupervised Machine Learning?
When we talk about machine learning and data science, we would be placing a lot of stress on the supervised aspects of it. In other words, we talk about algorithms that are performing well on the test set only when they are training used supervision. Nonetheless, there is data available in various formats which is unlabeled. Therefore, interesting insights and trends could be found with this unlabeled data. Following are some examples of unsupervised machine learning.
Customer Segmentation
In this machine learning approach, we would be classifying customers based on a specific set of attributes and commonalities between the features of the customers respectively. As a result, various groups are created based on which different selling strategies could be employed which would in turn help in directing the business to those customers.
Recommender System
In this machine learning problem, the data that we would be working on would not contain a dependent variable and would only consist of independent variables. Therefore, it would be an unsupervised machine learning problem where there is no presence of the target or output variable. We are given a dataset that contains different users and their ratings for different movies. We get a sparse matrix as not every user would rate all the movies and the list of movies is also large. Therefore, the machine learning models could come from the unsupervised approach such as K — means clustering or association rules.
Products Segmentation
Similar to how customer segmentation is done, product segmentation is also performed with the aid of unsupervised machine learning. Products that are identical to other products are grouped together depending on the features chosen. After performing many iterations, especially with k-means, it would accurately segment the product information so that we come to a conclusion about certain properties of a product. As a result, customers take into account the products that are similar to each other before making a purchase decision.
DNA Sequence Segmentation
When there is a huge amount of data in the form of DNA samples and sequences, scientists and experts in cell research sometimes fail to accurately identify and segment the DNA sequences. This is due to the fact that the volume of the sequences getting generated and identified is increasing many-fold. Unsupervised machine learning is used in the process of segmenting the images and understanding them. Therefore, this is also an interesting application of unsupervised machine learning respectively.
Conclusion
After reading this article, you might have a good understanding of how unsupervised machine learning works along with its uses in various industries. Hope you found this article helpful. Feel free to share your feedback and suggestions. Thanks.
