![Numerical measure of how alike two data
objects are. Is higher when objects are more
alike. Often falls in the range [0,1]](https://image.slidesharecdn.com/aippt-221101131844-80cec5c4/85/R-Ramya-devi-artificial-intelligence-and-machine-learning-7-320.jpg)
R Ramya devi artificial intelligence and machine learning
- 1. DEPARTMENT OF COMPUTER SCIENCE R.RAMYA DEVI II-MSC(CS)
- 2. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
- 3. K-Nearest Neighbors (KNN) Simple, but a very powerful classification algorithm Classifies based on a similarity measure Non-parametric Lazy learning Does not “learn” until the test example is given Whenever we have a new data to classify, we find its K-nearest neighbors from the training data
- 4. Classified by “MAJORITY VOTES” for its neighbor classes Assigned to the most common class amongst its Knearest neighbors (by measuring “distant” between data)
- 5. Voronoi diagram Describes the areas that are nearest to any given point, given a set of data. Each line segment is equidistant between two points of opposite class
- 6. Learning and implementation is extremely simple and Intuitive Flexible decision boundaries
- 7. Numerical measure of how alike two data objects are. Is higher when objects are more alike. Often falls in the range [0,1]
- 8. Irrelevant or correlated features have high impact and must be eliminated Typically difficult to handle high dimensionality Computational costs: memory and classification time computation
- 9. Numerical measure of how different are two data objects Lower when objects are more alike Minimum dissimilarity is often 0 Upper limit varies
- 10. Euclidean Distance 𝑑𝑖𝑠𝑡 = σ𝑘=1 𝑝 (𝑎𝑘 − 𝑏𝑘 ) 2 Where p is the number of dimensions (attributes) and 𝑎𝑘 and 𝑏𝑘 are, respectively, the k-th attributes (components) or data objects a and b. Standardization is necessary, if scales differ
- 11. THANK YOU