Five Most Popular Unsupervised Learning Algorithms

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Unsupervised Learning Algorithms

   Today we are going to learn about the popular unsupervised learning algorithms in machine learning. Before that let’s talk about a fun puzzle.

Have you ever done a complete-the-pattern puzzle? 

Where, we do some shapes of different designs presented in a row, and you have to suppose what the next form is going to be.

It is interesting, right? 

Although we have never seen those sorts of puzzles before, we are still able to figure it rightly (Haha, not every time)

So, what we are doing here is pattern recognition. It depends on what we see and guess a trend or pattern in the given data.

We analyze the whole data. Draw some conclusions, and, based on that, predict the next occurring shape or design.

Learn the most popular unsupervised learning algorithms in machine learning #machinelearning #datascience #python #clustering 



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Well, unsupervised learning algorithms also follow the same approach for solving the real-world problems. 

In this article, we are going to discuss different unsupervised machine learning algorithms. We will also cover the proper functioning of these unsupervised machine learning algorithms.

This unsupervised machine learning algorithms article help you like a quick recap for brush up the topics you can refer while you are preparing for the data science jobs.

Before we begin, let’s look at the topics you are going to learn.

Let’s start the article by discussing unsupervised learning.

What is Unsupervised Machine learning?

Unsupervised Learning Example

Unsupervised learning is a machine learning approach in which models do not have any supervisor to guide them. Models themselves find the hidden patterns and insights from the provided data. 

It mainly handles the unlabelled data. Somebody can compare it to learning, which occurs when a student solves problems without a teacher’s supervision. 

We cannot apply unsupervised learning directly to a regression or classification problem. Because like supervised learning, we don’t have the input data with the corresponding output label. 

Unsupervised learning aims to discover the dataset’s underlying pattern, assemble that data according to similarities, and express that dataset in a precise format.

Unsupervised Learning Algorithms allow users to perform more advanced processing jobs compared to supervised learning.

However, unsupervised learning can be more irregular compared with other methods. 

Example:  

Assume we have x input variables, then there would be no corresponding output variable. The algorithms need to find an informative pattern in the given data for learning.

Why use an Unsupervised Learning algorithm?

There are various reasons which illustrate the importance of Unsupervised Learning:

  1. It is similar to how a  human learns. It involves thinking by experiences, which moves it closer to real AI.

  2. It works on unlabeled data, which makes unsupervised learning further critical as real-world data is mostly unlabelled. 

  3. It helps look for useful insights from the data.

By now, we have covered all the basics of unsupervised learning. Now, let us discuss different unsupervised machine learning algorithms. 

Types of Unsupervised Learning Algorithms

Unsupervised Learning Algorithms

There are the following types of unsupervised  machine learning algorithms: 

Let us analyze them in more depth.

K-means Clustering

K-means Clustering

K-Means Clustering is an Unsupervised Learning algorithm. It arranges the unlabeled dataset into several clusters. 

Here K denotes the number of pre-defined groups. K can hold any random value, as if K=3, there will be three clusters, and for K=4, there will be four clusters. 

It is a repetitive algorithm that splits the given unlabeled dataset into K clusters. 

Each dataset belongs to only one group that has related properties. It enables us to collect the data into several groups. 

It is a handy method to identify the categories of groups in the given dataset without training.

How does the K-means algorithm work

The functioning of the K-Means algorithm describes as following :

  1. Choose the number K to determine the number of clusters.
  2. Select arbitrary K points or centroids. (It can be different from the input dataset).
  3. Assign all data points to their nearest centroid. It will create the predetermined  K clusters.
  4. Calculate the variance and put a new centroid of each cluster.
  5. Repeat the third step. Keep reassigning each data point to the latest cluster’s closest centroid.
  6. If any reassignment happens, then move to step-4; else, end.
  7. Finally, your model is ready.

There are several difficulties with K-means. It regularly seeks to make clusters of a similar size. 

Additionally, we have to determine the number of groups at the starting of the algorithm. We do not know how many clusters we have to choose from at the starting of the algorithm. It’s a challenge with K-means.

If you would like to learn more about the k-means clustering algorithm please check the below article.

Hierarchical clustering

Hierarchical Clustering Algorithm

Hierarchical clustering, also known as Hierarchical cluster analysis. It is an unsupervised clustering algorithm. It includes building clusters that have a preliminary order from top to bottom.

For example, All files and folders on the hard disk are in a hierarchy.

The algorithm clubs related objects into groups named clusters. Finally, we get a set of clusters or groups. Here each cluster is different from the other cluster. 

Also, the data points in each cluster are broadly related to each other.

Hierarchical Clustering Types Agglomerative and Divisive

Two types of Hierarchical clustering method are:

  1. Agglomerative Hierarchical Clustering
  2. Divisive Hierarchical Clustering

Agglomerative Hierarchical Clustering

 In an agglomerative hierarchical algorithm, each data point is considered a single cluster. Then these clusters successively unite or agglomerate (bottom-up approach) the clusters’ sets. The hierarchy of the clusters is shown using a dendrogram.

Divisive Hierarchical Clustering

In a divisive hierarchical algorithm, all the data points form one colossal cluster. The clustering method involves partitioning (Top-down approach) one massive cluster into several small clusters.

How does Agglomerative Hierarchical Clustering Works

The functioning of the K-Means algorithm is :

  • Consider each data point as a single cluster. Hence, we will have, say, K clusters at the beginning. The number of data points is also K at the beginning.
  • In this step, we have to make a big cluster by merging the two closest data points. We will get a total of K-1 clusters.
  • Next, to make more clusters, we have to merge two closest clusters. It will result in K-2 clusters.
  • Now, to create one big cluster repeat the above three steps till K becomes 0. We will repeat this till no data points remaining for joining.
  • Finally, after making one massive cluster, dendrograms are divided into various clusters according to the problem.

It is a beneficial approach to segmentation. The benefit of not pre-defining the number of clusters provides it an edge over K-Means. But, it doesn’t work fine when we have a huge dataset.

If you would like to learn more about the hierarchical clustering algorithm please check the below article.

Anomaly Detection

The detection of anomalies comprises distinguishing rare and unusual events. The ideal approach to anomaly detection is calculating a detailed summary of standard data.

Each newly arrived data point is compared to the normality model, and an anomaly score is determined. 

The score specifies the variations of the new instance from the average data instance. If the deviation exceeds a predefined threshold, the data point is considered an anomaly or outlier. It is easy to handle then.

Detection of anomalies is an unsupervised learning algorithm. There exist a large number of applications practicing unsupervised anomaly detection methods. 

It is essential to determine the outliers in various applications like medical imaging, network issues, etc.

Detection of anomalies is most useful in training situations where we have various instances of regular data. It lets the machine come near to the underlying population leading to a concise model of normality.

How does Anomaly Detection Work?

To detect anomalies, we have observations x1,. . . , xn ∈ X. The underlying presumption is, most of the data come from the same (unknown) distribution. We call it  normalization in data. 

However, some observations come from a different distribution. They are considered anomalies. Several reasons can lead to these anomalies. 

The final task is to identify these anomalies by observing a concise description of the standard data so that divergent observations become outliers.

Principal Component Analysis

How Principal Component Analysis Works

Principal Component Analysis is an unsupervised learning algorithm. We use it for dimensionality reduction in machine learning. 

A statistical approach transforms the observations of correlated features into a collection of linearly uncorrelated components using orthogonal transformation. 

These new transformed features are known as the Principal Components. It is one of the most popular machine learning algorithms.

 PCA is used for exploratory data analysis and predictive modeling. It is a way to identify hidden patterns from the given dataset by lessening the variances. It follows a feature extraction technique. 

PCA usually tries to express the lower-dimensional surface to project the high-dimensional data. PCA determines the variance of each feature. 

The feature with high variance shows the excellent split between the classes and hence reduces the dimensionality. 

PCA is used in image processing, movie recommendation systems, etc. PCA considers the required features and drops the least important attributes.

How does the PCA algorithm work?

Collect your dataset.

  1. Arrange data into a structure
  2. Normalizing the given data
  3. Calculate the Covariance of Z
  4. Determine the EigenValues and EigenVectors
  5. Sort the calculated EigenVectors
  6. Assess the new features Or Principal Components
  7. Drop unimportant features from the new dataset.

Apriori algorithm

The Apriori algorithm is a categorization algorithm. The Apriori algorithm uses frequent data points to create association rules. 

It works on the databases that hold transactions. The association rule determines how strongly or how feebly two objects are related. 

This algorithm applies a breadth-first search to choose the itemset associations. It helps in detecting the common itemsets from the large dataset.R. Agrawal and Srikant in 1994 proposed this algorithm.

Market basket analysis uses the apriori algorithm. It supports finding those commodities that we buy together. It is also helpful in the healthcare department.

How does the Apriori Algorithm work?

There are the following steps for the apriori algorithm:

  1. Define the support of itemsets in the transactional database. Then,  choose the minimum support and confidence.
  2. Select all supports in the transaction with a higher support value than the minimum support value.
  3. Determine all the subsets’ rules, which have a higher confidence value compared to the threshold confidence.
  4. Sort the rules in the decreasing order of weight.

For an artificial neural network, we can use the apriori algorithm. It helps in dealing with large datasets and sort data into categories.

If you would like to learn more about the PCA algorithm please check the below article.

Conclusion

That’s it for this article. In this article, we discussed all the crucial unsupervised learning algorithms used in field of machine learning.

These algorithms play a significant role when dealing with real-world data. So, a proper understanding of these algorithms is required. 

I hope you’ve enjoyed reading this article. Share this article and give your valuable feedback in the comments.

What Next

In this article, we covered all the basics of unsupervised learning. Next, you can check the practical implementation of these algorithms on our platform.

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