This article explains unsupervised learning and how it works from an artificial intelligence (AI) perspective.
Unsupervised learning is a type of machine learning used to identify patterns in sets of unlabeled data.
Unsupervised learning algorithms find patterns in large unsorted data sets without human guidance or supervision.
They can group data points within vast sets, allowing them to draw insights faster and more efficiently than any human data scientist.
The machine learning process is completely automated once the algorithm is fed the unstructured data. Ideally, these algorithms will improve at real-time categorization as they establish new relationships between data points (or inputs).
For instance, an unsupervised learning algorithm given images of different shapes might start sorting each shape according to its size and color. Then, the algorithm may get more specific by classifying shapes based on their number of sides.
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Unsupervised learning has been helpful in many areas of AI, including:
Unsupervised learning is often used with supervised learning, which relies on training data labeled by a human. In supervised learning, a human decides the sorting criteria and outputs of the algorithm.
This gives people more control over the types of information they want to extract from large data sets. However, supervised learning requires more human time and expertise.
An unsupervised approach is appropriate when you have a large quantity of unorganized data. With unsupervised learning, no one needs to analyze or label anything. Thus, unsupervised learning costs less than supervised learning since it requires less human labor.
Semi-supervised learning algorithms combine both approaches by comparing labeled and unlabeled data in the initial training set.
The results of unsupervised learning can be unpredictable and sometimes even unhelpful.
If the algorithm gets too specific, it might create too many categories, making it difficult for humans to draw meaningful insights from the outputs. On the flip side, if the algorithm is too general, there will be too few categories.
Accuracy can be hard to verify since all the data is unlabeled, and it can be difficult to determine how exactly unsupervised learning algorithms make their decisions.
Unsupervised learning takes more computing power and time, but it's still cheaper than supervised learning because no human involvement is needed.
Many unsupervised learning algorithms are based on cluster analysis, or clustering, which involves grouping objects based on their similarities and differences. Some of the methods unsupervised learning algorithms use include:
K clustering, often referred to as K-means clustering, is when data is organized based on similarity and also how the clusters are different from one another. K is used to represent the number of clusters.
This is the method of gathering information about information. So, once the data has been gathered, it's then sorted into similar groups and then, finally, organized into sections and subsections. Some of the more fiscally responsible among us do this already by clustering down our spending into shelter, home, and transportation. But when you cluster even further, you'll see transportation could be further clustered into mass transit, our car, etc. And then, under car, you might also have maintenance, fuel, cleaning, and so on. Computers do this on far grander scales and many different sets of data, and typically not about how much latte it consumes before 10:30 am.