Unsupervised learning refers to the training of an AI system using information that is not classified or labelled. What this ideally means is that the algorithm has to act on the information without any prior guidance.
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In unsupervised learning, the machine groups unsorted/unordered information regarding similarities and differences. This is done without the provision of categories for the machine to categorize the data into. The systems that use such learning are generally associated with generative learning model.
How does Unsupervised Machine Learning work?
In unsupervised learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms. Subjecting a system to unsupervised learning is an established way of testing the capabilities of that system.
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Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems. However, unsupervised learning can be more unpredictable than the alternate model. A system trained using the unsupervised model, might, for example, figure out on its own how to differentiate cats and dogs, it might also add unexpected and undesired categories to deal with unusual breeds, which might end up cluttering things instead of keeping them in order.
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For unsupervised learning algorithms, the AI system is presented with an unlabeled and uncategorized data set. The thing to keep in mind is that this system has not undergone any prior training. In essence, unsupervised learning can be thought of as learning without a teacher.
In case of supervised learning, the system has both the inputs and the outputs. So depending on the difference between the desired output and the observed output, the system is set to learn and improve. However, in the case of unsupervised learning, the system only has inputs and no outputs.
Unsupervised learning comes in extremely helpful during the tasks associated with data mining and feature extraction. The ultimate goal of unsupervised learning is to discover hidden trends and patterns in the data or to extract desired features. Like we said earlier, unsupervised learning only deals with the input data set without any prior knowledge or learning. Therefore, there are two types of unsupervised learning:
Parametric Unsupervised Learning
Parametric unsupervised learning assumes a parametric distribution of data. What this means, is that this type of unsupervised learning assumes that the data comes from a population that follows a particular probability distribution based on some parameters. In theory, if we consider a normal distribution of a family of objects, then we’ll see that all the members have some similar characteristic and are always parametrized by mean and standard deviation. This means that if we know the mean and standard deviation, and if the distribution is normal, then we can very easily find out the probability of future observations. Parametric Unsupervised Learning is much harder than the standard supervised learning because there are no labels available; hence there is no predefined measure of accuracy to test the output.
Non-parametric Unsupervised Learning
Non-parametric unsupervised learning refers to the clustering of the input data set. Each cluster, in essence, says something about the categories and classes of the data items present in the set. This is the most commonly used method for data modelling and analyzing data with small sample sizes. These methods are also referred to as distribution-free methods because unlike in the case of parametric learning, the modeller doesn’t need to make any assumptions about the distribution of the whole population.
At this point, it is essential to dive a bit into what do we mean by clustering.
So, what is clustering?
Clustering is one of the most important underlying concepts when it comes to unsupervised learning. It deals with finding a structure or pattern in a collection of uncategorized data. A simple definition of a cluster could be “the process of grouping the object into classes such that each member of a class is similar to the other in one or the other way.”
Therefore, a cluster can be simply defined as a collection of data objects which are “similar” between a cluster and “dissimilar” to the objects of the other cluster.
Applications of unsupervised machine learning
The goal of unsupervised machine learning is to uncover previously hidden patterns and trends in the data. But, most of the time, the data patterns are poor approximations of what supervised machine learning can achieve – for example, they segment customers into large groups, rather than treating them as individuals and delivering highly personalized communications. In the case of unsupervised learning, we do not know what the outcome will be, and hence, if we need to design a predictive model, supervised learning makes more sense in real-world context.
The ideal use-case for using unsupervised machine learning is when you don’t have data on desired outcomes. For instance, if you need to determine a target market for an entirely new product. However, if you want to categorize your consumer base better, supervised learning is the better option.
Let’s look at some applications of unsupervised machine learning techniques:
- Unsupervised learning is extremely helpful for anomaly detection from your dataset. Anomaly detection refers to finding significant data points in your collection of data. This comes in quite handy for finding out fraudulent transactions, discovering broken pieces of hardware, or identifying any outliers that might have crept in during data entry.
- Association mining means identifying a set of items that occur together in a dataset. This is quite a helpful technique for basket analysis as it allows analysts to discover good often purchased together. Association mining is not possible without clustering the data, and when you talk clustering, you talk unsupervised machine learning algorithm.
- One more use-case of unsupervised learning is dimensionality reduction. it refers to reducing the number of features in a dataset and thereby enabling better data preprocessing. Latent variable models are commonly used for this purpose and are made possible only by using unsupervised learning algorithms.
The patterns and trends uncovered using unsupervised learning can also come in handy when applying supervised learning algorithms later on – for example, unsupervised learning may help you perform cluster analysis on a dataset, and then you can use supervised learning on any cluster of your choice/need.
All in all, machine learning and artificial intelligence are incredibly complex fields, and any sophisticated AI system you come across will most probably be using a combination of various learning algorithms and mechanisms. Having said that, if you’re a beginner, it is imperative that you know the key points revolving around all the primary learning techniques.
We hope we were able to clarify the subtler points of an unsupervised learning algorithm. If you have a doubt, please drop it in the comments below!
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Mention some limitations of unsupervised machine learning.
In the case of unsupervised machine learning, human intervention may be necessary to grasp the diverse patterns and link them with domain expertise, which might be costly. The output or findings are frequently found to be inaccurate. An unsupervised task's sorting and output cannot be precisely defined. It is highly dependent on the model and, as a result, on the machine. Furthermore, the total number of courses is unknown. As a result, the conclusions of the analysis are hard to verify.
What is the association rule in unsupervised machine learning?
The association rule is one of several unsupervised machine learning methods. You may create relationships between data elements in huge databases using association rules. Discovering intriguing correlations between variables in massive databases is the goal of this unsupervised approach/algorithm. The association rule is a set of approaches that are used to offer a great base for generating all kinds of predictions and assessing the chances of certain occurrences happening over others.