Understanding Unsupervised Learning

While we were in our childhood days, we learned a lot from our parents, siblings, or people around us, but there are certain things we know and learn about from our own experiences. This is mostly unconsciously through the identification of patterns in our environment and applying them to circumstances placed in front of us. When speaking about the ML and AI world, unsupervised learning works similarly. In today's article, we'll touch upon unsupervised learning and the common approaches to this method.

Unsupervised Learning: A Brief Introduction

Unsupervised learning, also referred to as unsupervised machine learning, adopts ML algorithms to evaluate and cluster unlabeled datasets. Without requiring human intervention, these algorithms identify occult patterns or data clusters. The ability to establish differences and similarities makes unsupervised learning an ideal solution concerning customer segmentation, data analysis, image recognition, and cross-selling strategies.

Common unsupervised learning approaches

Speaking about the various approaches, there are three main tasks that unsupervised learning models are leveraged for. They are clustering, association, and dimensionality reduction. So, here, we'll discuss each learning method in detail. Let's get started.

Clustering

Unlabeled data is grouped using the data mining method of clustering based on similarities or differences. The processing of unclassified, raw data objects into groups that are modeled after the data's patterns or structures is known as clustering. There are several different types of clustering algorithms, including exclusive, overlapping, hierarchical, and probabilistic ones.

Exclusive and Overlapping Clustering

A data point may only be included in one cluster when using an exclusive clustering method of grouping. This method of clustering is also known as hard clustering. Exclusive clustering is exemplified by the K-means clustering algorithm.

Data points are divided into K groups according to the distance from each group's centroid in the K-means clustering method, which is a typical illustration of an exclusive clustering technique. The clustering of data into categories will depend on which data points are closest to a given centroid. In contrast to smaller K values, which indicate larger groupings and less granularity, larger K values indicate smaller groupings.

Data points can be members of multiple clusters with different degrees of membership under overlapping clusters, which is different from exclusive clustering. Overlapping clustering is demonstrated by "soft" or fuzzy k-means clustering.

Hierarchical clustering

As a type of unsupervised clustering algorithm, hierarchical clustering, also referred to as hierarchical cluster analysis (HCA), can be divided into two groups: agglomerative and divisive. The term "bottoms-up approach" refers to aggregative clustering. Its data points are initially separated into distinct groupings, and once a cluster has been achieved, iteratively combining them based on similarity is performed.

To ensure similarity can be measured, four different techniques are leveraged:

Ward's linkage: The increase in the sum squared following the merging of two clusters serves as a measure of the distance between them.

Average linkage: The mean distance between any two points within a cluster serves as the basis for this method.

Complete linkage: The maximum distance between any two points in a cluster serves as the definition for this method.

Single linkage: This method is represented by the minimum distance between two points in a cluster.

Although Manhattan distance and other metrics are also mentioned in clustering literature, Euclidean distance is the most popular metric used to calculate these distances.

Divisive clustering is different from agglomerative clustering in way that it leverages the strategy of "top-down". The distinctions between the data points in this instance are used to divide a single data cluster. In the context of hierarchical clustering, divisive clustering is not frequently employed, but it is still important to note. Dendrograms, which resemble trees and show how data points are combined or divided at each iteration, are commonly used to represent these clustering processes.

Probabilistic clustering

If we need to solve "soft" clustering problems or resolve density estimation, a probabilistic model is the most ideal pick, which is a supervised earning method. When we follow a probabilistic clustering model, we organize data points into groups. It's based on how certain it is that they belong to a specific distribution. One of the most popular probabilistic clustering methods, the Gaussian Mixture Model has been leveraged in various applications.

Given that they fall under the category of mixture models, Gaussian Mixture Models are composed of an arbitrary number of probability distribution functions. When we use GMMs, we try to specify to which distribution a given data point belongs, is it a Gaussian or normal probability distribution. We can even do this if we are cognizant of the mean or variance. If the task is to figure out the probability of assigning a set data point to a specific cluster, the expectation-maximization algorithm is the frequent pick.

Association Rules

We leverage the association rule-based approach when we want to establish a connection between the variables in a dataset. Companies can better understand the relationships between various products by using these techniques, which are frequently used for market basket analysis. Businesses can create more effective cross-selling techniques and recommendation engines by understanding the consumption patterns of their customers. If you check out Amazon or Spotify, you'll get a few examples of this, mostly from Discover Weekly (Spotify) or Customers Who Bought This Item Also Bought (Amazon). While several different algorithms, including Apriori, Eclat, and FP-Growth, are employed to produce association rules, the Apriori algorithm is the most frequently employed.

Dimensionality reduction

More data generally produces more accurate results, but it can also affect how machine learning algorithms perform (for example, overfitting) and make it challenging to visualize datasets. When there are too many features or dimensions in a dataset, the dimensionality reduction technique is used. The dataset's integrity is maintained as much as possible while the number of data inputs is reduced to a manageable number. When preprocessing data, it is frequently used.

Final Words

We are now at the concluding parts of the article. To summarize what we've discussed, we first went through a short introduction to unsupervised learning and discussed the common approaches of this method, which include clustering, association rules, and dimensionality reduction.

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