- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
What Are Self Organizing Maps: Beginner’s Guide
Updated on 15 March, 2023
6.46K+ views
• 11 min read
Table of Contents
Do you ever feel like you’re swimming in data but don’t know how to understand it? Data is becoming more readable and less complex with rapid advances in data sciences, machine learning, and artificial intelligence.
Self organizing maps are an example of one such advancement that reduces the dimensionality of data to reveal correlations that would otherwise be difficult to decipher. Self organizing maps (SOMs) use an unsupervised learning approach to cluster and map data to unravel complex issues and problems. With machine learning expected to reach a two trillion dollar valuation by 2030, this is the right time to upskill and learn about SOM in machine learning.
Enrol for the Machine Learning Course from the World’s top Universities. Earn Master, Executive PGP, or Advanced Certificate Programs to fast-track your career.
If you want a headstart in the right direction to understand the self organizing maps, you are in the right place. Read on to know more!
What is a Self-Organizing Map?
Self organizing maps (SOM) were introduced in the 1980s by the Finnish computer scientist Teuvo Kalevi Kohonen, also known as Kohonen’s Map after him. Self organizing maps are an example of Artificial Neural Networks that reduces data dimensionality through self-organising neural networks that support knowledge-based processing. Drawing inspiration from the structure and functioning of the human neural system, neural networks process and develop algorithms to untangle complex patterns, correlations, and problems.
Self organizing maps are unsupervised neural networks trained through unsupervised and competitive learning algorithms. The networks develop their classifications without any external or specified target output. Hence, they are ‘self-organizing.’
The maps consist of two layers- the input layer and the output layer. By clustering and mapping, they take higher dimensional data sets and reduce them to a lower dimensional, discretised representation- usually two-dimensional- called a map. It helps simplify multidimensional, complex data while preserving the topological properties of the input layer.
Uses Of Self-Organizing Maps
Self organizing maps are the way of the future. The discretised representation of multidimensional training data simplifies complex issues. The critical function of transforming a higher dimensional dataset into a lower dimensional representation holds the key to uncomplicating training data. It makes data visualization easier for the human eye.
It does so without the threat of data loss from reducing training data into a lower dimensional output or dimensionality reduction. Unlike in Principal Component Analysis (PCA), self organizing maps have an advantage as they retain the topological or structural information of the training data lost in PCA. Therefore, in cases where all dimensions are essential, they are represented in the Kohonen map despite reducing the data into two-dimensional outer space.
Further, seismic facies analysis helps recognise and develop organized relational clusters or groups by identifying different individual features. Self organizing maps act as a calibration method that relates these clusters to physical reality in the absence of physical analogs.
Additionally, self organizing maps aid in text clustering. This critical preprocessing step enables verification of text to decipher how it can be converted to a mathematical expression through SOM and further analysed and processed. Moreover, SOM helps in exploratory data analysis by revealing underlying and hidden patterns, relationships, and groups within training data through clustering and visualisation.
Consequently, SOM in machine learning and artificial intelligence has many applications across fields- from pattern recognition, medical applications, telecommunications, robotics, product management, data mining and processing, and more!
Architecture and Functionality of Self-Organizing Maps
The architecture of self organizing maps is essential in understanding what they do and how they do it. SOMs consist of two layers of nodes- the input layer and the output layer (or the Kohonen layer or the SOM layer). The two layers are directly connected. The input layer consists of source nodes that express features, attributes, or variables. They are represented as m-dimensional input vectors, x = (x₁, x₂…xₘ). The output layer, or the Kohonen layer, has nodes arranged in topological architecture, which is usually two-dimensional with a grid organisation consisting of rows and columns.
Each node has a specific location in the grid, and each input vector has a corresponding weight vector, w = (w₁, w₂…wₘ). These nodes indicate the maximum number of clusters possible from the input data. The adjacency of nodes depicts similarity between clusters, and the distance between neighbours is unimportant. The map thus takes on different shapes, typically forming rectangular or hexagonal grids. Each topological structure has specific properties, and the hexagonal is the preferred version.
Now that we know the basic architecture of self-organising maps, let’s understand how they function. We will breakdown the functionality of SOMs into the following steps:
1. Initialisation of weights
The first step that initiates the mapping process in self organizing maps is the initialisation of weights to vectors. Random values are selected for the initial weight vectors (wₒ).
2. Sampling
A sample of the input training vector (x) is chosen randomly from the input space.
3. Similarity matching
Nodes compete to be activated and selected in this stage of the competition. The node whose weight vector is closest to the input vector becomes activated by computing their similarity using measurement methods. The most viable equation to measure distance [d₀ (t)] is the Euclidean distance for visual representation. The winning node is called the Best Matching Unit (BMU).
4. Identify neighbourhood
In this step, the topological neighbourhood radius [nr(t)] of the BMU [c(t)] is identified. In this stage, the process of cooperation takes place.
5. Weight updating
It is the stage of adaptation in which the weight vectors of the BMU and nodes that fall within the neighbourhood in the output space are updated using the weight updation equation. It helps nodes in the output space closely resemble and represent the features of the input space. Two parameters are essential: learning rate [α(t)] and neighbourhood size.
6. Continuation
The process from step b onwards is repeated for N iterations till the feature map stops changing and takes on an identifiable shape.
Source: Example of how Self Organizing Maps work
Advantages
Self-organising maps are valuable in simplifying data to reveal the underlying patterns and relationships. There are several advantages of SOMs.
- SOMs make complex and multidimensional data easy to understand and read because of reduced data dimensionality and clustering.
- Self organizing maps do not cause data loss as the input data is preserved in the topological representation.
- It retains the topological relations of the input space through clustering in outer space.
- SOMs can negotiate an array of classification issues while providing a comprehensive and valuable summary at the same time.
- They help in easier data visualisation.
Disadvantages
There are several advantages of self organizing maps, but they also have certain disadvantages.
- SOMs require a large amount of good-quality training data.
- The computational costs of self organizing maps are very high.
- SOMs take a comparatively long time to prepare and train in the face of slowly evolving data.
- They are not a good fit for categorical and mixed-type data.
- The initial weight vector influences cluster patterns.
- It is difficult to determine the optimal map size.
Implementing Self-Organizing Maps Using Python
Here is an example code for implementing a SOM using Python:
import numpy as np
from matplotlib import pyplot as plt
class SOM:
def __init__(self, input_shape, output_shape, learning_rate=0.1, sigma=1.0):
self.input_shape = input_shape
self.output_shape = output_shape
self.learning_rate = learning_rate
self.sigma = sigma
self.grid = np.random.randn(*output_shape, input_shape)
def train(self, data, num_epochs):
for epoch in range(num_epochs):
for x in data:
winner = self._find_winner(x)
self._update_weights(x, winner)
def _find_winner(self, x):
x = np.expand_dims(x, axis=0)
distances = np.linalg.norm(self.grid – x, axis=-1)
return np.unravel_index(np.argmin(distances), self.output_shape)
def _update_weights(self, x, winner):
winner_weight = self.grid[winner]
distances = np.linalg.norm(np.indices(self.output_shape) – np.array(winner)[:, np.newaxis, np.newaxis], axis=0)
influence = np.exp(-distances ** 2 / (2 * self.sigma ** 2))
self.grid += self.learning_rate * influence[…, np.newaxis] * (x – winner_weight)
def get_map(self):
return self.grid.reshape(-1, self.input_shape)
Best Machine Learning and AI Courses Online
Let’s go through this code step by step:
- First, we import NumPy and Matplotlib, which we’ll use for numerical operations and visualisation, respectively.
- Next, we define a class SOM to encapsulate our SOM implementation. The __init__ method takes three arguments: input_shape is the dimensionality of the input space, output_shape is the shape of the SOM grid, and learning_rate and sigma are hyperparameters for the learning rate and neighbourhood size, respectively. We initialise the grid of neurons randomly.
- The train method takes two arguments: data is the input data to train the SOM on, and num_epochs is the number of training epochs. For each epoch, we iterate over each input vector in data and update the weights of the neurons using the _update_weights method.
- The _find_winner method takes an input vector x and returns the neuron’s index in the grid with the closest weight vector to x. To do this, we compute the Euclidean distance between x and each neuron’s weight vector and return the index of the neuron with the smallest distance.
- The _update_weights method takes an input vector x and the index of the winning neuron winner and updates the weight vectors of all neurons in the grid based on their distance to the winning neuron. We compute the Euclidean distance between each neuron’s index and winner and use this to compute an influence function that determines how much each neuron’s weight vector should be updated. The influence function is a Gaussian function of the distance, with a width controlled by the sigma hyperparameter. We update each neuron’s weight vector by adding a fraction of the difference between x and the winning neuron’s weight vector, scaled by the influence function and the learning rate.
- Finally, the get_map method returns a flattened version of the grid, where each row corresponds to the weight vector of a neuron.
Here’s an example of how you can use this SOM implementation to cluster a dataset:
# Generate some sample data
data = np.random.randn(100, 2)
# Create a SOM with a 10×10 grid
som = SOM(input_shape=2, output_shape=(10, 10))
# Train the SOM for 100 epochs
som.train(data, num_epochs=100)
# Get the map of the SOM
map = som.get_map()
# Plot the data and the SOM
plt.scatter(data[:, 0], data[:, 1], color=’blue’)
plt.scatter(map[:, 0], map[:, 1], color=’red’)
plt.show()
In this example, we generate a dataset of 100 2-dimensional vectors using NumPy’s randn function. We create a SOM with a 10×10 grid and train it on the dataset for 100 epochs. Finally, we get the map of the SOM and plot it along with the original data using Matplotlib.
This should give you a good starting point for implementing SOMs in Python. The basic SOM algorithm has many variations and extensions, so feel free to experiment and explore!
In-demand Machine Learning Skills
Conclusion
The immensity of available data today can make recognising the existing correlations, relationships, and patterns challenging. Converting this data into an easily readable and digestible model can be critical to forming actionable insights to solve real-world problems and issues. Self-organizing maps are an example of technological advancement that can benefit humanity. Clustering and mapping higher dimensional data into lower dimensional models while preserving all the topological properties make SOM in machine learning highly applicable across fields.
If you want to be a part of the ever-evolving and advancing field of Machine Learning, look no further.
Step into the future with upGrad.
Popular AI and ML Blogs & Free Courses
Sign up today for upGrad’s Advanced Certificate Programme in Machine Learning and NLP. Offered by IIIT Bangalore, the 8-month course from India’s #1 Technical University (Private) will jumpstart your career in the industry. The comprehensive curriculum includes subjects like Machine Learning, Natural Language Processing, Machine Translation, and Git, taught by an experienced faculty.
Enrol now and elevate your career by becoming part of an illustrious alumni network!
You can also check out our free courses offered by upGrad in Management, Data Science, Machine Learning, Digital Marketing, and Technology. All of these courses have top-notch learning resources, weekly live lectures, industry assignments, and a certificate of course completion – all free of cost!
Frequently Asked Questions (FAQs)
1. What are the three processes involved in training Self Organizing Maps?
The three processes involved in training self-organizing maps are competition, cooperation, and adaptation, which are part of the competitive learning process of the algorithm.
2. What is Competitive Learning?
SOMs differ from other artificial neural networks as they rely on competitive learning methods. Competitive learning can be defined as an artificial neural network learning process where different neurons or processing elements compete on who is allowed to learn to represent the current input.
3. What is Unsupervised Training in SOM?
Unsupervised training, or unsupervised machine learning, is the technique of analysing and clustering unlabeled datasets to identify hidden patterns and relations.
RELATED PROGRAMS