What does Unsupervised Machine Learning Mean?

Unsupervised machine learning algorithms infer patterns from a dataset without reference to known, or labeled, outcomes. Unlike supervised machine learning, unsupervised machine learning methods cannot be directly applied to a regression or a classification problem because you have no idea what the values for the output data might be, making it impossible for you to train the algorithm the way you normally would. Unsupervised learning can instead be used to discover the underlying structure of the data.

Why is Unsupervised Machine Learning Important?

Unsupervised machine learning purports to uncover previously unknown patterns in data, but most of the time these patterns are poor approximations of what supervised machine learning can achieve. Additionally, since you do not know what the outcomes should be, there is no way to determine how accurate they are, making supervised machine learning more applicable to real-world problems.
The best time to use unsupervised machine learning is when you do not have data on desired outcomes, such as determining a target market for an entirely new product that your business has never sold before. However, if you are trying to get a better understanding of your existing consumer base, supervised learning is the optimal technique.
Some applications of unsupervised machine learning techniques include:

• Clustering allows you to automatically split the dataset into groups according to similarity. Often, however, cluster analysis overestimates the similarity between groups aaand doesn’t treat data points as individuals. For this reason, cluster analysis is a poor choice for applications like customer segmentation and targeting.
• Anomaly detection can automatically discover unusual data points in your dataset. This is useful in pinpointing fraudulent transactions, discovering faulty pieces of hardware, or identifying an outlier caused by a human error during data entry.
• Association mining identifies sets of items that frequently occur together in your dataset. Retailers often use it for basket analysis, because it allows analysts to discover goods often purchased at the same time and develop more effective marketing and merchandising strategies.
• Latent variable models are commonly used for data preprocessing, such as reducing the number of features in a dataset (dimensionality reduction) or decomposing the dataset into multiple components.

Types of Unsupervised Learning Algorithm:

The unsupervised learning algorithm can be further categorized into two types of problems:

Regression models

Regression tasks are different, as they expect the model to produce a numerical relationship between the input and output data. Examples of regression models include predicting real estate prices based on zip code, or predicting click rates in online ads in relation to time of day, or determining how much customers would be willing to pay for a certain product based on their age.
Algorithms commonly used in supervised learning programs include the following:

• Clustering: Clustering is a method of grouping the objects into clusters such that objects with most similarities remains into a group and has less or no similarities with the objects of another group. Cluster analysis finds the commonalities between the data objects and categorizes them as per the presence and absence of those commonalities.
• Association: An association rule is an unsupervised learning method which is used for finding the relationships between variables in the large database. It determines the set of items that occurs together in the dataset. Association rule makes marketing strategy more effective. Such as people who buy X item (suppose a bread) are also tend to purchase Y (Butter/Jam) item. A typical example of Association rule is Market Basket Analysis.

Unsupervised Learning algorithms:

Below is the list of some popular unsupervised learning algorithms:

• K-means clustering
• KNN (k-nearest neighbors)
• Hierarchal clustering
• Anomaly detection
• Neural Networks
• Principle Component Analysis
• Independent Component Analysis
• Apriori algorithm
• Singular value decomposition

Advantages of Unsupervised Learning

• Unsupervised learning is used for more complex tasks as compared to supervised learning because, in unsupervised learning, we don't have labeled input data.
• Unsupervised learning is preferable as it is easy to get unlabeled data in comparison to labeled data.

Disadvantages of Unsupervised Learning

• You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known
• Less accuracy of the results is because the input data is not known and not labeled by people in advance. This means that the machine requires to do this itself.
• The spectral classes do not always correspond to informational classes.
• The user needs to spend time interpreting and label the classes which follow that classification.
• Spectral properties of classes can also change over time so you can’t have the same class information while moving from one image to another.