Christmas Special : Upto 40% OFF! + 2 free courses  - SCHEDULE CALL

sddsfsf

Understanding Divisive Hierarchical Clustering in Data Science

 

Clustering is a popular technique used in data science to group similar objects together based on their characteristics. It helps identify patterns and relationships within the data, which can be useful for various applications such as customer segmentation, image recognition, and anomaly detection. One of the clustering techniques that are widely used in hierarchical clustering is divisive clustering. In this post, we will discuss divisive hierarchical clustering - its definition, advantages, disadvantages, and how to implement it using Python. Before diving into divisive clustering and learning more about its importance in data science or data mining and key takeaways. You should check out the data science tutorial guide to understand basic concepts. 

 What is Divisive Hierarchical Clustering?

Divisive hierarchical clustering is a popular technique in various fields, such as biology, computer science, and data mining. This method is advantageous when dealing with large datasets where it may not be feasible to group objects based on their similarities or differences manually.One of the primary advantages of divisive hierarchical clustering is that it provides a complete hierarchy of clusters, allowing for a more detailed analysis of the relationships between different objects. Additionally, this method can handle different data types, including numeric values and categorical variables.

Steps to Divisive Hierarchical Clustering 

The algorithm for divisive hierarchical clustering involves several steps. 

Step 1: Consider all objects a part of one big cluster. 

Step 2: Spilt the big cluster into small clusters using any flat-clustering method- ex. k-means.

Step 3: Selects an object or subgroup to split into two smaller sub-clusters based on some distance metric such as Euclidean distance or correlation coefficients. 

Step 4: The process continues recursively until each object forms its own cluster.

For example, suppose we have a dataset of customer information such as age, income level, and purchase history. Using divisive hierarchical clustering, we could group customers based on their similarities in these attributes to identify potential target markets for marketing campaigns or product development efforts. If you are interested in a career path for data science, we have a complete guide to help you with your new career opportunities and growth.

Implementing Divisive Hierarchical Clustering in Python

To implement divisive hierarchical clustering using Python, we can use the SciPy library, which provides a function called "linkage" that performs hierarchical clustering. Here's an example code snippet:

Advantages of Divisive Hierarchical Clustering

Divisive hierarchical clustering is a clustering algorithm that divides a dataset into smaller subgroups or clusters based on certain criteria. There are numerous advantages to using this technique in hierarchical clustering.

  • The main advantage of this technique is its ability to provide a clear hierarchy of clusters at different levels, making it easy for users to choose the number and granularity of clusters they want. For example, imagine we have a large dataset with thousands of data points representing customer preferences for different products. Using divisive hierarchical clustering, we can divide the data into broad categories, such as electronics, fashion, food items, etc., and then further divide each category into more specific subcategories, such as laptops, smartphones, shirts, etc. This allows us to create custom segments tailored to our business needs without re-running the algorithm multiple times.
  • Another advantage of divisive hierarchical clustering is its scalability - it works well even with very large datasets because it only requires pairwise distances between objects rather than computing all possible combinations like other algorithms such as K-means. This means that computation time remains manageable even when dealing with millions or billions of data points.However, there are also some limitations associated with this approach. One challenge is determining the appropriate threshold level at which to stop dividing clusters - if we set the threshold too high, we may end up with overly general groups that don't capture enough detail; At the same time, if we set it too low, we risk ending up with too many small groups that aren't useful for analysis.
  • Divisive hierarchical clustering effectively segments complex datasets and uncovers meaningful insights about customer behaviors or market trends. By providing a clear hierarchy of clusters at different levels and working well even with large amounts of data, it has become an increasingly popular tool in fields ranging from marketing research and e-commerce analytics to scientific studies in biology and ecology, where similar techniques are used to classify organisms based on shared characteristics.

Disadvantages of Divisive Hierarchical Clustering

Divisive Clustering in data science is an effective technique of clustering, but it comes with its own limitations. 

  • One disadvantage of divisive clustering is that it can be computationally expensive when dealing with large datasets due to its recursive nature.
  • Another disadvantage is that the quality of results depends heavily on the choice of distance metric used for splitting clusters. Choosing an inappropriate distance metric may result in poor performance.
  • A third disadvantage of divisive clustering is that it can lead to overfitting. This occurs when the algorithm continues to divide clusters until each data point is in its own cluster, resulting in a model that fits the training data perfectly but performs poorly on new, unseen data. It is important to set stopping criteria for the algorithm to prevent overfitting and carefully evaluate the results.
  • Additionally, divisive clustering may not be suitable for datasets with complex structures or non-linear relationships between variables. Other clustering methods like k-means or density-based clustering may be more appropriate in such cases.
  • Another potential issue with divisive clustering is that it requires all data points to belong to a single cluster at the start of the algorithm. This means that outliers or noise in the dataset can significantly impact the final results.
  • Finally, interpreting and visualizing dendrograms produced by divisive clustering can be challenging as they tend to become large and complex quickly as more clusters are created. Careful examination of dendrograms and consideration of alternative visualization techniques may be necessary to gain insight into patterns within clustered data.

Data Science Training For Administrators & Developers

  • No cost for a Demo Class
  • Industry Expert as your Trainer
  • Available as per your schedule
  • Customer Support Available
cta9 icon

Conclusion 

Divisive clustering is a powerful technique for grouping similar objects together based on their characteristics. It provides a clear hierarchy of clusters at different levels of clustering techniques with large datasets. However, it can be computationally expensive when dealing with large datasets and requires a careful selection of distance metrics to achieve optimal results. By implementing this algorithm in Python using the SciPy library, we can easily perform divisive clustering on our data and gain valuable insights into its structure and relationships. If you’re looking to improve your skill sets or begin your career in the world of data, you may enroll yourself in some of the top data science certification courses.

Trending Courses

Cyber Security icon

Cyber Security

  • Introduction to cybersecurity
  • Cryptography and Secure Communication 
  • Cloud Computing Architectural Framework
  • Security Architectures and Models
Cyber Security icon1

Upcoming Class

1 day 27 Dec 2024

QA icon

QA

  • Introduction and Software Testing
  • Software Test Life Cycle
  • Automation Testing and API Testing
  • Selenium framework development using Testing
QA icon1

Upcoming Class

2 days 28 Dec 2024

Salesforce icon

Salesforce

  • Salesforce Configuration Introduction
  • Security & Automation Process
  • Sales & Service Cloud
  • Apex Programming, SOQL & SOSL
Salesforce icon1

Upcoming Class

4 days 30 Dec 2024

Business Analyst icon

Business Analyst

  • BA & Stakeholders Overview
  • BPMN, Requirement Elicitation
  • BA Tools & Design Documents
  • Enterprise Analysis, Agile & Scrum
Business Analyst icon1

Upcoming Class

1 day 27 Dec 2024

MS SQL Server icon

MS SQL Server

  • Introduction & Database Query
  • Programming, Indexes & System Functions
  • SSIS Package Development Procedures
  • SSRS Report Design
MS SQL Server icon1

Upcoming Class

1 day 27 Dec 2024

Data Science icon

Data Science

  • Data Science Introduction
  • Hadoop and Spark Overview
  • Python & Intro to R Programming
  • Machine Learning
Data Science icon1

Upcoming Class

8 days 03 Jan 2025

DevOps icon

DevOps

  • Intro to DevOps
  • GIT and Maven
  • Jenkins & Ansible
  • Docker and Cloud Computing
DevOps icon1

Upcoming Class

-0 day 26 Dec 2024

Hadoop icon

Hadoop

  • Architecture, HDFS & MapReduce
  • Unix Shell & Apache Pig Installation
  • HIVE Installation & User-Defined Functions
  • SQOOP & Hbase Installation
Hadoop icon1

Upcoming Class

2 days 28 Dec 2024

Python icon

Python

  • Features of Python
  • Python Editors and IDEs
  • Data types and Variables
  • Python File Operation
Python icon1

Upcoming Class

1 day 27 Dec 2024

Artificial Intelligence icon

Artificial Intelligence

  • Components of AI
  • Categories of Machine Learning
  • Recurrent Neural Networks
  • Recurrent Neural Networks
Artificial Intelligence icon1

Upcoming Class

9 days 04 Jan 2025

Machine Learning icon

Machine Learning

  • Introduction to Machine Learning & Python
  • Machine Learning: Supervised Learning
  • Machine Learning: Unsupervised Learning
Machine Learning icon1

Upcoming Class

1 day 27 Dec 2024

 Tableau icon

Tableau

  • Introduction to Tableau Desktop
  • Data Transformation Methods
  • Configuring tableau server
  • Integration with R & Hadoop
 Tableau icon1

Upcoming Class

2 days 28 Dec 2024