This document discusses unsupervised learning algorithms, specifically clustering. It defines clustering as a machine learning technique that groups unlabeled datasets into clusters of similar data points without supervision. Popular clustering algorithms mentioned include K-means clustering and hierarchical clustering. The key advantages of unsupervised learning are that it can handle more complex tasks since the data is unlabeled, and unlabeled data is easier to obtain than labeled data. However, the results may be less accurate since the algorithms do not know the exact outputs.

1. P1WU
UNIT – III: CLASSIFICATION
Topic 2: UNSUPERVIZED ALGORITHMS -
CLUSTERING
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2. UNIT III
1.A Characterization of Text
Classification
2. Unsupervised
Algorithms: Clustering
3. Naïve Text Classification
4. Supervised Algorithms
5. Decision Tree
6. k-NN Classifier
7. SVM Classifier
8. Feature Selection or
Dimensionality Reduction
9. Evaluation metrics
10. Accuracy and Error
11. Organizing the classes
12. Indexing and Searching
13. Inverted Indexes
14. Sequential Searching
15. Multi-dimensional
Indexing
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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
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3. INTRODUCTION TO UNSUPERVIZED ALGORITHMS
• Below is the list of some popular unsupervised learning algorithms:
• K-means clustering
• KNN (k-nearest neighbors)
• Hierarchal clustering
• Anomaly detection
• Neural Networks
• Principle Component Analysis
• Independent Component Analysis
• Apriori algorithm
• Singular value decomposition
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
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4. INTRODUCTION TO UNSUPERVIZED ALGORITHMS
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
CS8080- INFORMATION RETRIEVAL TECHNIQUES

5. WHAT ARE CLUSTERING?
• Clustering or cluster analysis is a
machine learning technique, which
groups the unlabelled dataset.
• It can be defined as "A way of
grouping the data points into
different clusters, consisting of
similar data points. The objects with
the possible similarities remain in a
group that has less or no similarities
with another group."
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
CS8080- INFORMATION RETRIEVAL TECHNIQUES

6. WHAT ARE CLUSTERING?
• It does it by
• finding some similar patterns in the unlabelled dataset
such as shape, size, color, behavior, etc., and divides them
as per the presence and absence of those similar patterns.
• It is an unsupervised learning method,
• hence no supervision is provided to the algorithm, and it
deals with the unlabeled dataset.
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
CS8080- INFORMATION RETRIEVAL TECHNIQUES

7. Difference between Supervised and Unsupervised Learning
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
CS8080- INFORMATION RETRIEVAL TECHNIQUES
Supervised Learning Unsupervised Learning
Supervised learning algorithms aretrained using labeled data. Unsupervised learning algorithmsare trained using unlabeled data.
Supervised learning model takesdirect feedback to check if it is
predicting correct output or not.
Unsupervised learning model doesnot take any feedback.
Supervised learning model predictsthe output. Unsupervised learning model findsthe hidden patterns in data.
Supervised learning needs supervision to train the model. Unsupervised learning does not needany supervision to train the model.
Supervised learning can becategorized
in Classification and Regression problems.
Unsupervised Learning can beclassified in Clustering and
Associations problems.
Supervised learning can be used for those cases where we
know theinput as well as corresponding outputs.
Unsupervised learning can be used for those cases where we have
onlyinput data and no corresponding output data.
Supervised learning model produces an accurate result. Unsupervised learning model may give less accurate result as compared
to supervised learning.
It includes various algorithms such It includes various algorithms such

8. Advantages of Unsupervised Learning
• Unsupervised learning is used for more complex tasks
as compared to supervised learning because,
• in unsupervised learning, we don't have labeled input data.
• Unsupervised learning is preferable as
• it is easy to get unlabeled data in comparison to labeled
data.
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
CS8080- INFORMATION RETRIEVAL TECHNIQUES

9. Disadvantages of Unsupervised Learning
• Unsupervised learning is
• intrinsically more difficult than supervised learning as it does not have
corresponding output.
• The result of the unsupervised learning algorithm might be
• less accurate as input data is not labeled, and algorithms do not know the
exact output in advance.
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
PROFESSIONAL ELECTIVE – IV
CS8080- INFORMATION RETRIEVAL TECHNIQUES

10. Any Questions?
AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
SEMESTER – VIII
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