Top 10 Unsupervised ML Models to Learn in the Year 2023

The top unsupervised ML models to learn in 2023 help find patterns and make decisions based on input data

Machine learning algorithms are classified into several types: Supervised learning, Unsupervised learning, Reinforcement learning, Deep learning, Semi-supervised learning, and Generative models. When compared to supervised learning, unsupervised ML models enable users to perform more complex processing tasks. It can be more unpredictable than other natural learning methods.

 Unsupervised learning is when it can provide a set of unlabeled data, which is required to analyze and find patterns inside. Dimension reduction and clustering are two examples. Clustering, anomaly detection, neural networks, and other unsupervised learning algorithms are other examples.  The machine is trained using data that has not been labeled, classified, or categorized, and the algorithm is required to operate on that data without supervision. Unsupervised learning seeks to restructure the input record into new features or a set of objects with similar patterns. Here are the top 10 unsupervised ML models to learn in 2023:

1. Hierarchical Clustering

Hierarchical clustering is a clustering algorithm that creates a hierarchy of clusters. It starts with all of the data, which is assigned to its cluster. Two close clusters will be in the same cluster in this case. When there is only one cluster left, the algorithm terminates.

2. K-means Clustering

K denotes an iterative clustering algorithm that assists you in determining the highest value for each iteration. At first, the desired number of clusters is chosen. This clustering method requires you to divide the data points into k groups. A higher k indicates smaller groups with greater granularity. Lower k values indicate larger groups with less granularity. The algorithm produces a set of "labels." It allocates each data point to one of the k groups. Each group in k-means clustering is defined by defining a centroid for each group.

3. K-nearest Neighbors

The K-nearest neighbor classifier is the most basic of all machine learning classifiers. It is distinct from other machine learning techniques in that it does not generate a model. It is a straightforward algorithm that stores all available cases and classifies new instances using a similarity metric. When there is a large gap between examples, it works very well.

4. Principle Component Analysis

Principal Component Analysis is an unsupervised learning algorithm used in machine learning to reduce dimensionality. It is a statistical process that uses an orthogonal transformation to convert observations of correlated features into a set of linearly uncorrelated features. It is one of the most widely used tools for exploratory data analysis and predictive modeling. It is a method for extracting strong patterns from a given dataset by reducing variances.

5. Independent Component Analysis

Independent Component Analysis (ICA) is a machine-learning technique for distinguishing independent sources in a mixed signal. Unlike principal component analysis, which seeks to maximize data point variance, the independent component analysis seeks to maximize independence, i.e., independent components.

6. Gaussian Mixture Models

Gaussian mixture models (GMMs) are a type of ML algorithm used to categorize data into various groups based on probability distribution. Gaussian mixture models have numerous applications, including finance, marketing, and many others.

7. Anomaly Detection

Anomaly detection is the technique of identifying rare events or observations that are statistically different from the rest of the observations and can raise suspicions. Such "abnormal" behavior usually indicates a problem, such as credit card fraud, a failing machine in a server, a cyber attack, and so on. The anomaly can be broadly classified into three types: Point Anomaly, Contextual Anomaly, and Collective anomaly.

8. Apriori Algorithm

The Apriori algorithm generates association rules using frequent item sets and is intended to work on transactional databases. It determines how strongly or weakly two objects are connected using these association rules. To efficiently calculate the itemset associations, this algorithm employs a breadth-first search and a Hash Tree. It is the iterative process of locating frequent itemsets in a large dataset.

9. Frequent Pattern Growth

Han In proposed the FP-Growth Algorithm in. This is a fast and scalable method for mining the entire set of frequent patterns using pattern fragment growth and an extended prefix-tree structure for storing compressed and important information about frequent patterns called the frequent-pattern tree (FP-tree). Han demonstrated in his study that his method outperforms other popular methods for mining frequent patterns, such as the Apriori Algorithm and TreeProjection.

10. Neural Networks

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning that form the foundation of deep learning algorithms. Their name and structure are inspired by the human brain, and they mimic the way biological neurons communicate with one another. Artificial neural networks (ANNs) are made up of node layers, each of which has an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, is linked to another and has its weight and threshold.