Clustering is a subset of unsupervised learning algorithms and is often used in the fields of data mining, statistical data analysis, and machine learning. Clustering is the process of separating similar samples (or points) in a data set into groups (or “clusters”). Similarity is often based on distance between samples.
For example, imagine you want to segment the customers of an e-commerce site into different segments. Customers' purchasing history, demographic information, clicking behavior, etc. You can put similar customers in the same cluster by using features such as. In this way, each cluster represents a specific customer segment.
Segmentation generally refers to the process of dividing a data set into smaller, manageable subsets or “segments.” This is often done to facilitate processing of large data sets. Segmentation may not generally be based on the structure or distribution of the data and is usually done randomly or based on a specific dimension.
For example, if you have a data set of one million rows, you can split it into 10 partitions of 100,000 rows. This makes it possible to process data in parallel and obtain faster results, especially when working with large data sets.
Unsupervised learning algorithms.
Unsupervised learning is a machine learning approach used in situations where labeled target variables are not available. Such algorithms are used to find structures or relationships between input data. Here are some popular algorithms used for unsupervised learning:
K-Means Clustering: K-means is a clustering algorithm that separates similar data points into groups.
Hierarchical Clustering: This algorithm creates a hierarchy based on similarities between items in the data set.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): It is a density-based clustering algorithm and works especially well on noisy data sets.
PCA (Principal Component Analysis): PCA is a technique used to reduce the size of data.
t-SNE (t-Distributed Stochastic Neighbor Embedding): t-SNE is used to visualize high-dimensional data into a low-dimensional space.
Autoencoders: Autoencoders are often used in deep learning and learn a dense representation of data.
Apriori: This algorithm is often used to find frequently repeated sets of items from databases.
One-hot Encoding: It is a method used to convert categorical variables into numbers.
These algorithms can be used for a variety of applications, such as discovering hidden structures in a dataset, separating data into clusters, dimensionality reduction, and more. Which algorithm to use often depends on the requirements of the application and the nature of the data.
How many categories of unsupervised learning?
Unsupervised learning is generally divided into two main categories: Clustering and Associative learning.
Clustering: Clustering algorithms bring together similar data points in a data set into groups or “clusters.” These algorithms discover hidden structures or groups in the data set. Algorithms such as K-Means, Hierarchical Clustering and DBSCAN are included in this category.
Association Learning: Relational learning algorithms find relationships or rules between elements in the data set. These types of algorithms are often used in applications such as market basket analysis, where we can find out how often the purchase of a particular product is associated with the purchase of another product. Algorithms such as Apriori and FP-Growth are in this category.
Besides this, unsupervised learning also includes techniques such as dimensionality reduction and feature extraction. These techniques are often used to reduce the complexity of the data and make it more understandable. Methods such as Principal Component Analysis (PCA) and t-SNE are in this category.
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