How different K-NN is from K-Means Algorthim ? How do you pick the optimal K- Value ?
They are often confused with each other. The 'K' in K-Means Clustering has nothing to do with the 'K' in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification. k-NN is a supervised algorithm used for classification. What this means is that we have some labeled data upfront which we provide to the model for it to understand the dynamics within that data i.e. train. It then uses those learnings to make inferences on the unseen data i.e. test. In the case of classification this labeled data is discrete in nature. k-Means is an unsupervised algorithm used for clustering. By unsupervised we mean that we don’t have any labeled data upfront to train the model. Hence the algorithm just relies on the dynamics of the independent features to make inferences on unseen data.
The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value.
How different K-NN is from K-Means Algorthim ? How do you pick the optimal K- Value ?
They are often confused with each other. The 'K' in K-Means Clustering has nothing to do with the 'K' in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification. k-NN is a supervised algorithm used for classification. What this means is that we have some labeled data upfront which we provide to the model for it to understand the dynamics within that data i.e. train. It then uses those learnings to make inferences on the unseen data i.e. test. In the case of classification this labeled data is discrete in nature. k-Means is an unsupervised algorithm used for clustering. By unsupervised we mean that we don’t have any labeled data upfront to train the model. Hence the algorithm just relies on the dynamics of the independent features to make inferences on unseen data.
The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value.
5. How different k-NN is from k-Means algorithm? How do you pick the optimal k-value? (see...
Question 2. Use the greedy algorithm to color the graph below, ordering the vertices alphabetically. Is this coloring optimal? How do you know? Question 2. Use the greedy algorithm to color the graph below, ordering the vertices alphabetically. Is this coloring optimal? How do you know?
Question 4 1 pts Which of the following reasons is not the reason why the K-means algorithm will likely end up with sub-optimal clustering? (Select all that apply.) Bad choices for the initial cluster centers. Choosing a k that corresponds to the number of natural clusters in the dataset. Fast convergence of the K-means algorithm. Existence of closely located data samples in the dataset. Question 5 1 pts Which of the following is a step in K-means algorithm implementation? (Select...
3. Consider a Gaussian mixture model with K component Gaussians with different means having the same covariance matrix Σ but all How would you modify the equations of the Expectation Maximization algorithm in order to take into account the fact that the covariance matrix is the same for all components? Justify your answer. 3. Consider a Gaussian mixture model with K component Gaussians with different means having the same covariance matrix Σ but all How would you modify the equations...
a) Why is implementing a K-means clustering algorithm multiple times with a fixed K important to do? 119 b) Why is cross-validation preferred over resubstituting as a method to measure classification accuracy? Explain c) Give two situations when nearest neighbor classification may be preferred over linear and quadratic discriminant analysis methods in general. Explain your answer. a) Why is implementing a K-means clustering algorithm multiple times with a fixed K important to do? 119 b) Why is cross-validation preferred over...
Data clustering and the k means algorithm. However, I'm not able to list all of the data sets but they include: ecoli.txt, glass.txt, ionoshpere.txt, iris_bezdek.txt, landsat.txt, letter_recognition.txt, segmentation.txt vehicle.txt, wine.txt and yeast.txt. Input: Your program should be non-interactive (that is, the program should not interact with the user by asking him/her explicit questions) and take the following command-line arguments: <F<K><I><T> <R>, where F: name of the data file K: number of clusters (positive integer greater than one) I: maximum number...
We were unable to transcribe this imageAs you can see from this equation, the value of t becomes larger as the difference between the means becomes larger, or the standard error of the difference in the means becomes smaller For example, consider two groups of data with the following parameters Group Mean 20 10 Sample variance 10 10 Sample size In this case 20-10 10 10 10 2+2 The last thing we need to calculate is known as the degrees...
Consider the RSA algorithm. Let the two prime numbers, p=11 and q=41. You need to derive appropriate public key (e,n) and private key (d,n). Can we pick e=5? If yes, what will be the corresponding (d,n)? Can we pick e=17? If yes, what will be the corresponding (d,n)? (Calculation Reference is given in appendix) Use e=17, how to encrypt the number 3? You do not need to provide the encrypted value.
How do you think McDonald's mission statement has focused the organization? What value do you see the mission statement providing to the organization and how might it be made better, if at all?
can you do part 4 & 5 for me 4. How do we define the average value of the function f(x) on the interval [a, b]? (see page 461 of the text) favg 5. Complete the Mean Value Theorem for Integrals on page 462 of the text. If f is continuous on [a, b], then there exists a number c in [a, b] such that f(c)- that is 4. How do we define the average value of the function f(x)...
So I managed to pick a problem from the textbook to do in class that had typos. I found another one with problems (OK, so I am making this up but it is a good story). Below you have incomplete data tables provided by a not so careful professor... Find the rate law, including the value of the rate constant k, and fill in the blanks for Exp, 5 and 6. There might not be enough space here for you...