What we search for every day, what we interact with on social media, what we purchase from online stores, our search history generates a big amount of data on a daily basis. Un-supervised learning is the sub-category of machine learning, which is employed to extract patterns from this massive amount of data.
In un-supervised learning, there is no direction of supervision from the humans involved. It is like exploring uncharted territory without a map and figuring out the direction on your own. The data is unlabeled, unstructured, and in large amounts and un-supervised learning involves identifying structures and patterns underlying that data.
Consider that a cybersecurity firm needs to ensure the protection of the network of its clients by any potential threats. Un-supervised learning is employed in that case so it can scan real time generated data and look for any deviation from the normal behavior. For example, a user can try to access a large volume of sensitive data at once or he may try to execute commands that are not reserved for his privilege. There may also be an unusually high number of logins from a single IP address. Behaviors like this are not typical in normal network traffic or system log data.
Un-supervised learning is also used to detect malware. This is done by analyzing file attributes, system calls, and behavioral patterns associated with known malware samples. This is how behavioral anomalies and security threats can be detected earlier and risks and mitigated quickly. Adaptability to new threats increases and false positives are reduced. Un-supervised learning proves to be far more accurate on real time data than any other traditional method of threat detection.
This is just one everyday scenario where un-supervised learning plays a crucial role.
Types of Un-Supervised Learning
There are three main types of un-supervised learning:
Clustering
Clustering algorithms classify the raw, unstructured, and unlabeled data into different clusters or groups based on their features or attributes. These groupings can be based on the similarities or differences among the items in the input dataset.
One of the applications of clustering is customer segmentation in e-commerce. Customers are grouped into different categories based on their demographic information, search history, products they interacted with and other relevant features. This is how marketers can tailor their marketing strategies, product campaigns etc. by identifying distinct groups of customers with similar preferences.
Association
Association algorithms are used to discover relationships and patterns between different variables in the input dataset. They aim to find different correlations, patterns, and associations among the items in the dataset.
It is used most frequently by businesses in market analysis. It is also used in the healthcare industry. A hospital’s electronic records are filled with an enormous dataset of patent records that contains their symptom details, diagnosis details, treatment plans, demographic information, related complications etc.
Association analysis is used to discover patterns and associations in that data. Co-occurrence of symptoms in a certain disease are discovered and the associated treatment plans are stumbled upon. The side effects and adverse reaction of certain drugs are also chanced on.
By leveraging all these insights, healthcare organizations can optimize their resource allocation, personalize patient treatments, and reduce the chance of false diagnosis.
Dimensionality Reduction
Dimensionality reduction aims to reduce the number of variables in a high-dimensional dataset as much as possible without losing the most important features. This algorithm is usually employed when there is a lot of noise data in the dataset and the chance of overfitting increases so the algorithm tries to cancel out the extra data for better and clearer data visualization.
Dimensionality reduction analysis is often used in image recognition systems. Images are high-dimensional data with thousands of pixels. Analysis of such data is computationally intensive and requires more resources. This is when dimensionality reduction comes into play as it detects the noisy and redundant features in the image and reduces the dimension of the picture by only preserving the part which is required for analysis. This lower-dimensional data is then easier to analyze further through different algorithms.
Remarks
Real time data is generated in enormous amounts on daily basis in every sector, be it healthcare, social media, finance and banking, cybersecurity, bioinformatics, marketing, and businesses – un-supervised learning is used to extract underlying patterns from this massive amount of data that we may not even realize are there. The algorithms and models involved extract these patterns on their own without any intervention and guidance from humans and play a crucial role in data mining, analysis, and visualization.
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