Guide to Unsupervised Machine Learning

The effective use of information is one of the prime requirements for any kind of business operation. At some point, the amount of data produced goes beyond simple processing capacities. That’s where machine learning kicks in.

However, before any of it could happen — the information needs to be explored and made sense of. That is what unsupervised machine learning is for in a nutshell.

In this article, we will explain what unsupervised machine learning really is and explore its major applications.

What is unsupervised machine learning?

Unsupervised learning is a type of machine learning algorithm that brings order to the dataset and enables to make sense of data.

Unsupervised machine learning algorithms are used to group unstructured data according to its similarities and distinct patterns in the dataset.

The term “unsupervised” refers to the fact that the algorithm is not guided like a supervised learning algorithm.

How does it work?

The unsupervised algorithm is handling data without prior training — it is a function that does its job with the data at its disposal. In a way, it is left at his own devices to sort things out as it sees fit.

The unsupervised algorithm works with unlabeled data. Its purpose is exploration. If supervised machine learning works under clearly defines rules, unsupervised learning is working under the conditions of results being unknown and thus needed to be defined in the process.

The unsupervised machine learning algorithm is used to:

1. explore the structure of the information;
2. extract valuable insights;
3. detect patterns;
4. implement this into its operation in order to increase efficiency.

In other words, it describes information — go through the thick of it and identifies what it really is.

In order to make that happen, unsupervised learning applies two major techniques — clustering and dimensionality reduction.

Let’s take a look at both of them.

Clustering — Exploration of Data

“Clustering” is the term used to describe the exploration of data. The clustering operation is twofold. The catch is that both parts of the process are performed at the same time.

Clustering involves:

• Defining the credentials that form the requirement for each cluster. The credentials are then matched with the processed data and thus the clusters are formed.
• Breaking down the dataset into the specific groups (known as clusters) based on their common features.

Clustering techniques are simple yet effective. They require some intense work yet can often give us some valuable insight into the data.

As such, it’s been used in many applications for decades including:

• Biology — for genetic and species grouping;
• Medical imaging — for distinguishing between different kinds of tissues;
• Market research — for understanding the different groups of customers based on some attributes
• Recommender systems — such as giving you better Amazon suggestions or Netflix movie matches.

Dimensionality reduction — Making data digestible

In a nutshell, dimensionality reduction is the process of distilling the relevant information. It can be also reiterated as getting rid of the unnecessary stuff.

The thing is — raw data is usually laced with a thick layer of data noise. It can be anything — missing values, erroneous data, muddled bits, simple something irrelevant to the cause. Because of that, before you start digging for insights — you need to clean it up first.

That’s what dimensionality reduction is for.

From the technical standpoint — dimensionality reduction is the process of decreasing the complexity of data while retaining the relevant parts of its structure to a certain degree.

Unsupervised Machine Learning Real Life Examples

k-means Clustering — Data Mining

K-means clustering is the central algorithm in unsupervised machine learning operation. It is the algorithm that defines the features present in the dataset and groups certain bits with common elements into clusters.

As such, k-means clustering is an indispensable tool in the data mining operation.

In addition to that — it is used in the following operations:

• Audience segmentation
• Customer persona investigation
• Anomaly detection (for example, to detect bot activity)
• Pattern recognition (grouping images, transcribing audio)
• Inventory management (by conversion activity or by availability)

Hidden Markov Model — Pattern Recognition, Natural Language Processing, data analytics

Hidden Markov Model is one of the more elaborate unsupervised machine learning algorithms. It is a statical model that analyzes the features of data and groups it accordingly.

Hidden Markov Model is a variation of the simple Markov chain that includes observations over the state of data. This adds another perspective on the data gives the algorithm more points of reference.

Hidden Markov Model major fields of use include:

• Optical Character recognition (including handwriting recognition)
• Speech recognition and synthesis (for conversational user interfaces)
• Text Classification (with parts-of-speech tagging)
• Text Translation

In addition to that, Hidden Markov Models are used in data analytics operations. In that field, HMM is used for clustering purposes. It finds the associations between the objects in the dataset and explores its structure. Usually, HMM are used for sound or video sources of information.

DBSCAN Clustering — Customer Service Personalization, Recommender engines

DBSCAN Clustering AKA Density-based Spatial Clustering of Applications with Noise is another approach to clustering. It is commonly used in data wrangling and data mining for the following activities:

• Explore the structure of the information
• Find common elements in the data
• Predict trends coming out of data

Overall, DBSCAN operation looks like this:

• The algorithm groups data points that are in close proximity to each other.
• Then it sorts the data according to the exposed commonalities

DBSCAN algorithms are used in the following fields:

• Targeted Ad Content Inventory Management
• Customer service personalization
• Recommender Engines

Principal component analysis (PCA) — Data Analytics Visualization / Fraud Detection

PCA is the dimensionality reduction algorithm for data visualization. It is a nice and simple algorithm that does its job and doesn’t mess around. In the majority of the cases is the best option.

In its core, PCA is a linear feature extraction tool. It maps the data in a linear manner in relation to the low-dimensional space.

PCA combines input features in a way that gathers the most important parts of data while leaving out the irrelevant bits.

As a visualization tool — PCA is good for showing a bird’s eye view on the operation. It can be a good tool to:

• Show the dynamics of the website traffic ebbs and flows.
• Break down the segments of the target audience on specific criteria

t-SNE — Data Analytics Visualization

t-SNE AKA T-distributed Stochastic Neighbor Embedding is another go-to algorithm for data visualization.

t-SNE uses dimensionality reduction to translate high-dimensional data into low-dimensional space. In other words, show the cream of the crop of the dataset.

The whole process looks like this:

• The algorithm counts the probability of similarity of the points in a high-dimensional space.
• Then it does the same thing in the corresponding low-dimensional space.
• After that, the algorithm minimizes the difference between conditional probabilities in high-dimensional and low-dimensional spaces for the optimal representation of data points in a low-dimensional space.

As such, t-SNE is good for visualizing more complex types of data with many moving parts and everchanging characteristics. For example, t-SNE is good for:

• Genome visualization in genomics application
• Medical test breakdown (for example, blood test or operation stats digest)
• Complex audience segmentation (with highly detailed segments and overlapping elements)

Singular value decomposition (SVD) — Recommender Systems

Singular value decomposition is a dimensionality reduction algorithm used for exploratory and interpreting purposes.

Basically, it is an algorithm that highlights the significant features of the information in the dataset and puts them front and center for further operation. Case in point — making consumer suggestions, such as which kind of shirt and shoes are fitting best with those ragged vantablack Levi’s jeans.

In a nutshell, it sharpens the edges and turns the rounds into the tightly fitting squares. In a way, SVD is reappropriating relevant elements of information to fit a specific cause.

SVD can be used:

• To extract certain types of information from the dataset (for example, take out info on every user located in Tampa, Florida).
• To make suggestions for a particular user in the recommender engine system.
• To curate ad inventory for a specific audience segment during real-time bidding operation.

Association rule — Predictive Analytics

Association rule is one of the cornerstone algorithms of unsupervised machine learning.

It is a series of technique aimed at uncovering the relationships between objects. This provides a solid ground for making all sorts of predictions and calculating the probabilities of certain turns of events over the other.

While association rules can be applied almost everywhere, the best way to describe what exactly they are doing are via eCommerce-related example.

There are three major measure applied in association rule algorithms

• Support measure shows how popular the item is by the proportion of transaction in which it appears.
• Confidence measure shows the likeness of Item B being purchased after item A is purchased.
• Lift measure also shows the likeness of Item B being purchased after item A is purchased. However, it adds to the equation the demand rate of Item B.

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The secret of the gaining the competitive advantage on the specific market is in effective use of data.

Unsupervised machine learning algorithms let you discover the real value of the particular and find its place in the subsequent business operations. operation.

This article show how exactly this thing happens.

Got any ideas regarding data that require unsupervised learning? Go here!