List of Unsupervised ML Algorithms
List of common algorithms used in unsupervised machine
learning with more real-life examples for each:
1. K-Means Clustering:
- Explanation:
Partitions the data into K distinct clusters based on feature similarity.
- Real-Life
Examples:
- Customer
Segmentation: Grouping customers based on purchasing behavior for targeted
marketing.
- Image
Compression: Reducing the number of colors in an image by clustering
similar colors.
- Document
Clustering: Organizing a large set of documents into topics for easier
search and analysis.
2. Hierarchical Clustering:
- Explanation:
Builds a tree of clusters by recursively splitting or merging clusters.
- Real-Life
Examples:
- Organizing
Genes: Grouping genes with similar expression patterns in genomics
research.
- Customer
Hierarchies: Creating hierarchical customer segments based on purchasing
habits.
- Market
Research: Grouping survey respondents based on their responses to
understand different segments.
3. Principal Component Analysis (PCA):
- Explanation:
Reduces the dimensionality of data by transforming it into a set of linearly
uncorrelated variables called principal components.
- Real-Life
Examples:
- Image
Compression: Reducing the number of pixels while retaining essential
features in images.
- Finance:
Simplifying large datasets of financial indicators to identify key factors
driving market movements.
- Genomics:
Reducing the complexity of genetic data to identify patterns.
4. Independent Component Analysis (ICA):
- Explanation:
Separates a multivariate signal into additive, independent components.
- Real-Life
Examples:
- Blind
Source Separation: Isolating individual voices from a noisy recording.
- Brain
Imaging: Analyzing fMRI data to identify independent brain activity patterns.
- Financial
Data Analysis: Separating underlying factors that influence stock prices.
5. Association Rules:
- Explanation:
Discovers interesting relationships (rules) between variables in large
databases.
- Real-Life
Examples:
- Market
Basket Analysis: Identifying products frequently bought together to
optimize product placements.
- Online
Recommendations: Recommending items to users based on their browsing and
purchasing patterns.
- Medical
Research: Discovering associations between different symptoms and diseases.
6. DBSCAN (Density-Based Spatial Clustering of Applications with Noise):
- Explanation:
Groups together closely packed points, marking as outliers points that lie
alone in low-density regions.
- Real-Life
Examples:
- Crime
Analysis: Identifying areas of high crime activity in a city to allocate
police resources effectively.
- Astronomy:
Detecting clusters of stars and galaxies in astronomical data.
- Ecology:
Identifying clusters of animal sightings or plant occurrences in ecological
studies.
7. T-SNE (t-Distributed Stochastic Neighbor Embedding):
- Explanation:
Visualizes high-dimensional data by reducing it to two or three dimensions
while preserving the relative distances between points.
- Real-Life
Examples:
- Customer
Behavior Visualization: Understanding different customer segments in
e-commerce.
- Genomic
Data Visualization: Exploring patterns in high-dimensional genetic data.
- Social
Network Analysis: Visualizing relationships and community structures within
social networks.
8. Autoencoders:
- Explanation:
Neural networks used for unsupervised learning of efficient codings, typically
for the purpose of dimensionality reduction or feature learning.
- Real-Life
Examples:
- Image
Denoising: Removing noise from images while retaining essential features.
- Anomaly
Detection: Identifying unusual patterns in data that do not conform to
expected behavior.
- Data
Compression: Learning compact representations of data for efficient storage
and transmission.
These additional
examples illustrate the wide range of applications for each algorithm,
highlighting their versatility in uncovering patterns and structures in
unlabeled data.
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