Types of Unsupervised learning?
Introduction to Unsupervised learning in machine learning
Well, if you’re new to the subject of machine learning, you’ve definitely heard of various kinds of learning. If not, don’t worry, in this article, I’ll explain what unsupervised learning is.
Unsupervised learning
Unsupervised learning is a form of machine learning in which users are not required to give attention on the model. Instead, it gives the model the ability to function autonomously and discover details and patterns that were not previously known. The focus is primarily on unlabeled data.
Types of Unsupervised Learning
Clustering
Unlabeled data is grouped using the data mining approach of clustering based on how similar or dissimilar the data is. Algorithms called clustering divide raw, unclassified data objects into groups that can be represented by the data’s patterns or structures.
Types of algorithms used
- Hierarchical clustering
- K-means clustering
- Principal Component Analysis
- Singular Value Decomposition
- Independent Component Analysis
Association
An unsupervised learning technique called an association rule is used to uncover the connections among the variables in a sizable database. It establishes the group of items that co-occur in the collection.
Types of algorithms used
- Apriori
- Eclat
- FP-Growth
Dimensionality reduction
When a dataset has an excessive amount of characteristics or dimensions, the dimensionality reduction technique is utilised. It keeps the dataset’s integrity as much as feasible while reducing the quantity of data inputs to a tolerable level.
Types of algorithms used
- Principal component analysis
- Singular value decomposition
Challenges of unsupervised learning
Unsupervised learning has numerous advantages, but it can also present some difficulties because machine learning models can operate without any human involvement. These difficulties may include:
- Higher time to train models
- More time to train due to high volume data
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Conclusion
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