CS8080_IRT_UNIT - III T2 UNSUPERVISED ALGORITHMS -CLUSTERING.pdf
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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.
CS8080_IRT_UNIT - III T2 UNSUPERVISED ALGORITHMS -CLUSTERING.pdf
- 1. P1WU UNIT – III: CLASSIFICATION Topic 2: UNSUPERVIZED ALGORITHMS - CLUSTERING AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 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 AALIM MUHAMMED SALEGH COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SEMESTER – VIII PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 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 CS8080- INFORMATION RETRIEVAL TECHNIQUES
- 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 PROFESSIONAL ELECTIVE – IV CS8080- INFORMATION RETRIEVAL TECHNIQUES