Why Clustering?
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Application of Clustering in Data Science Using Real-Time Examples
The above video is the recorded session of the webinar on the topic “Application of Clustering in Data Science Using Real-Time Examples”, which was conducted on 28th June’14.
Introduction to Application of Clustering in Data Science
Clustering data into subsets is an important task for many data science applications. It is considered as one of the most important unsupervised learning technique. Keeping this in mind, we have come with a video that explains this with real life examples.
Topics included in this Video:
- What is Data Science?
- What is Clustering?
- What is K-means clustering?
- Examples of k-means clustering in real-life Data Science Applications.
Watch a Presentation on this Topic:
As you are aware, Clustering is “the process of organizing objects into groups whose members are similar in some way”. Let’s look at some of the reasons to go for clustering and where to use them.
Why Clustering?
Clustering can be done for the following reasons:
- Simplifications
- Pattern detection
- Useful in data concept construction
- Unsupervised learning process
Where to use Clustering?
Data Mining
- Information Retrieval
- Text mining
- Web Analysis
- Marketing
- Medical Diagnostic
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