The field of machine learning is one of the subsets of artificial intelligence that artificial intelligence solves by learning how to solve problems. If you are interested in this field and want to get information about this field, you should know that today data scientists use different algorithms in this field. They use the field.
Learning includes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In this article, we talk about the applications of unsupervised learning and describe its types for you. We will discuss supervised and unsupervised and learn the application of unsupervised learning.
Learning takes place with the help of several educational data or experiences. If we cannot write a program directly, we use learning.
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Supervised learning is a type of learning related to machine learning in which the input and output are specified and an observer provides information to the learner, and in this way, the system learns a function from input to output, in which data tagged are used.
For example, consider an email that is sent to you. The emails are incoming and the output is spam or non-spam. Spam is filtered. First, the data is divided into spam and non-spam and sent to the training machine. It is given, it is tested by the machine and you give an email to the machine that detects whether it is spam or not, in other words, the output is defined for our input.
Unsupervised learning is a machine learning method where users do not need to supervise the model. Instead, it allows the model to work on its own to discover previously undiscovered patterns and information, mostly dealing with unlabeled data.
In unsupervised learning, unlike supervised learning, the data is not specified in advance, and its purpose is not the connection between input and output, and only their classification is important, and the learner must look for a specific structure in the data.
An example of unsupervised machine learning
Let’s take for example a baby and his family dog, he recognizes and recognizes his family dog, and a few weeks later a family friend brings a new dog and tries to play with the baby. He has never seen this dog before. But recognizing many characteristics (2 ears, eyes, walking on 4 legs) that are like his pet dog, he identifies the new animal as a dog.
This is unsupervised learning, where you are not taught but learn from data (in this case data about a dog) if this learning was supervised, the family friend would tell the child it was a dog.
Why use unsupervised learning?
Here are the main reasons to use unsupervised learning:
- Unsupervised machine learning finds all kinds of unknown patterns in data.
- Unsupervised methods help you find features that can be useful for classification.
- Unsupervised learning takes place in real-time, so all input data must be analyzed and labeled in the presence of learners.
- Unlabeled data is easier to get from a computer than labeled data that requires manual intervention.
Unsupervised learning algorithms
Unsupervised learning algorithms allow users to perform more complex processing tasks compared to supervised learning. However, unsupervised learning can be unpredictable compared to other natural learning methods.
Unsupervised learning algorithms include clustering, contiguity method, Dimensionality Reduction algorithms, anomaly detection, neural networks, etc.
Clustering is an important concept during unsupervised learning. It mainly deals with finding a structure or pattern in an uncategorized data set. Clustering algorithms process your data and find if there are natural clusters (groups) in the data. You can also modify the number of clusters your algorithms should identify. It allows you to set the granularity of these groups, in other words, the data is divided into several groups with common attributes. Clustering is a form of unsupervised learning where you find patterns in the data you are working on that may be in the form of shape, size, etc., which is also used to group data items or create clusters.
Association rules allow you to establish relationships and associations among data in large databases. This unsupervised method is about discovering interesting relationships between variables in large databases. For example, people who buy a new house are more likely to buy new furniture, or people who buy product x tend to buy product y.
Dimensionality Reduction Algorithms
Dimensionality reduction is the transformation of data from a high-dimensional space to a low-dimensional space such that the low-dimensional representation preserves some meaningful features of the original data, ideally close to its inherent dimension.
It uses this model to reduce or combine variables that have little or no effect on the result, this algorithm is used together with the classification or regression algorithm.
Applications of unsupervised machine learning
Some applications of unsupervised machine learning techniques include:
- Clustering automatically divides data sets into groups based on their similarities.
- Anomaly detection can discover unusual data points in your data set, useful for finding fraudulent transactions.
- The Set Continuity method identifies items that often co-occur in your dataset.
- Latent variable models are widely used for data preprocessing. Such as reducing the number of features in a dataset or splitting the dataset into multiple parts.
Disadvantages of unsupervised learning
- You cannot get detailed information about the sorting of the data, and the output is not labeled and is known as the data used in unsupervised learning.
- The lower accuracy of the results is because the input data is unknown and has not been labeled by people in advance. This means that the device needs to do this itself.
- Spectral classes do not always correspond to informational classes.
- The user must spend time interpreting and labeling the classes that follow that classification.
- The spectral properties of classes can also change over time so you cannot have the same class information when moving from one image to another.