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Unsupervised Learning

What is unsupervised learning?

Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled.

The algorithms are thus allowed to classify, label, and/or group the data points contained within the data sets without having any external guidance in performing that task.

In other words, unsupervised learning allows the system to identify patterns within data sets on its own. The AI system will group unsorted information according to similarities and differences even though there are no categories provided

Unsupervised learning is sometimes also called unsupervised machine learning.

What’s the difference between unsupervised learning versus supervised learning?

The majority of practical machine learning uses supervised learning.

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems. Additionally, subjecting a system to unsupervised learning is one way of testing AI.

However, unsupervised learning can be more unpredictable than a supervised learning model. While an unsupervised learning AI system might, for example, figure out on its own how to sort cats from dogs, it might also add unforeseen and undesired categories to deal with unusual breeds, creating clutter instead of order.

AI systems capable of unsupervised learning are often associated with generative learning models, although they may also use a retrieval-based approach (which is most often associated with supervised learning). Chatbots, self-driving cars, facial recognition programs, expert systems, and robots are among the systems that may use either supervised or unsupervised learning approaches, or both.

How does unsupervised learning work?

Unsupervised learning starts when machine learning engineers or data scientists pass data sets through algorithms to train them.

There are no labels or categories contained within the data sets being used to train such systems; each piece of data that's being passed through the algorithms during training is an unlabeled input object or sample.

The objective with unsupervised learning is to have the algorithms identify patterns within the training data sets and categorize the input objects based on the patterns that the system itself identifies. The algorithms analyze the underlying structure of the data sets by extracting useful information or features from them.

Thus, these algorithms are expected to develop specific outputs from the unstructured inputs by looking for relationships between each sample or input object.

Algorithms may be given data sets containing images. The algorithms may then classify the items into categories. It may then group the images in increasingly more specific subgroups as it learns to identify distinctions within each category.

The algorithms do this by uncovering and identifying patterns, although in unsupervised learning this pattern recognition happens without the system having been fed data that teaches it to distinguish.

What are unsupervised learning algorithms used for?

Unsupervised learning models are used for three main tasks: 

1. Clustering

Clustering is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, etc.

2. Association

Association is another type of unsupervised learning method that uses different rules to find relationships between variables in a given dataset. These methods are frequently used for market basket analysis and recommendation engines, along the lines of “Customers Who Bought This Item Also Bought” recommendations.

3. Dimensionality reduction

Dimensionality reduction is a learning technique used when the number of features  (or dimensions) in a given dataset is too high. It reduces the number of data inputs to a manageable size while also preserving the data integrity. Often, this technique is used in the preprocessing data stage, such as when autoencoders remove noise from visual data to improve picture quality. 

What are some common unsupervised learning approaches?

Unsupervised learning models are utilized for three main tasks—clustering, association, and dimensionality reduction. Below we’ll define each learning method and highlight common algorithms and approaches to conduct them effectively.

1. Hierarchical clustering

Hierarchical clustering, also known as hierarchical cluster analysis (HCA), is an unsupervised clustering algorithm that can be categorized in two ways; they can be agglomerative or divisive. Agglomerative clustering is considered a “bottoms-up approach.” Its data points are isolated as separate groupings initially, and then they are merged together iteratively on the basis of similarity until one cluster has been achieved. 

2. Probabilistic clustering

A probabilistic model is an unsupervised technique that helps us solve density estimation or “soft” clustering problems. In probabilistic clustering, data points are clustered based on the likelihood that they belong to a particular distribution. The Gaussian Mixture Model (GMM) is the one of the most commonly used probabilistic clustering methods.


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Unsupervised Learning

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is unsupervised learning?

Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled.

The algorithms are thus allowed to classify, label, and/or group the data points contained within the data sets without having any external guidance in performing that task.

In other words, unsupervised learning allows the system to identify patterns within data sets on its own. The AI system will group unsorted information according to similarities and differences even though there are no categories provided

Unsupervised learning is sometimes also called unsupervised machine learning.

What’s the difference between unsupervised learning versus supervised learning?

The majority of practical machine learning uses supervised learning.

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems. Additionally, subjecting a system to unsupervised learning is one way of testing AI.

However, unsupervised learning can be more unpredictable than a supervised learning model. While an unsupervised learning AI system might, for example, figure out on its own how to sort cats from dogs, it might also add unforeseen and undesired categories to deal with unusual breeds, creating clutter instead of order.

AI systems capable of unsupervised learning are often associated with generative learning models, although they may also use a retrieval-based approach (which is most often associated with supervised learning). Chatbots, self-driving cars, facial recognition programs, expert systems, and robots are among the systems that may use either supervised or unsupervised learning approaches, or both.

How does unsupervised learning work?

Unsupervised learning starts when machine learning engineers or data scientists pass data sets through algorithms to train them.

There are no labels or categories contained within the data sets being used to train such systems; each piece of data that's being passed through the algorithms during training is an unlabeled input object or sample.

The objective with unsupervised learning is to have the algorithms identify patterns within the training data sets and categorize the input objects based on the patterns that the system itself identifies. The algorithms analyze the underlying structure of the data sets by extracting useful information or features from them.

Thus, these algorithms are expected to develop specific outputs from the unstructured inputs by looking for relationships between each sample or input object.

Algorithms may be given data sets containing images. The algorithms may then classify the items into categories. It may then group the images in increasingly more specific subgroups as it learns to identify distinctions within each category.

The algorithms do this by uncovering and identifying patterns, although in unsupervised learning this pattern recognition happens without the system having been fed data that teaches it to distinguish.

What are unsupervised learning algorithms used for?

Unsupervised learning models are used for three main tasks: 

1. Clustering

Clustering is a data mining technique for grouping unlabeled data based on their similarities or differences. For example, K-means clustering algorithms assign similar data points into groups, where the K value represents the size of the grouping and granularity. This technique is helpful for market segmentation, image compression, etc.

2. Association

Association is another type of unsupervised learning method that uses different rules to find relationships between variables in a given dataset. These methods are frequently used for market basket analysis and recommendation engines, along the lines of “Customers Who Bought This Item Also Bought” recommendations.

3. Dimensionality reduction

Dimensionality reduction is a learning technique used when the number of features  (or dimensions) in a given dataset is too high. It reduces the number of data inputs to a manageable size while also preserving the data integrity. Often, this technique is used in the preprocessing data stage, such as when autoencoders remove noise from visual data to improve picture quality. 

What are some common unsupervised learning approaches?

Unsupervised learning models are utilized for three main tasks—clustering, association, and dimensionality reduction. Below we’ll define each learning method and highlight common algorithms and approaches to conduct them effectively.

1. Hierarchical clustering

Hierarchical clustering, also known as hierarchical cluster analysis (HCA), is an unsupervised clustering algorithm that can be categorized in two ways; they can be agglomerative or divisive. Agglomerative clustering is considered a “bottoms-up approach.” Its data points are isolated as separate groupings initially, and then they are merged together iteratively on the basis of similarity until one cluster has been achieved. 

2. Probabilistic clustering

A probabilistic model is an unsupervised technique that helps us solve density estimation or “soft” clustering problems. In probabilistic clustering, data points are clustered based on the likelihood that they belong to a particular distribution. The Gaussian Mixture Model (GMM) is the one of the most commonly used probabilistic clustering methods.


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