Coursera: Machine Learning (Week 8) Quiz - Unsupervised Learning | Andrew NG

▸ Unsupervised Learning :

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1. For which of the following tasks might K-means clustering be a suitable algorithm
   Select all that apply.
   
   
   • Given a set of news articles from many different news websites, find out what are the main topics covered.

     K-means can cluster the articles and then we can inspect them or use other methods to infer what topic each cluster represents
     

   
   
   • Given historical weather records, predict if tomorrow’s weather will be sunny or rainy.
   
   
   • From the user usage patterns on a website, figure out what different groups of users exist.

     We can cluster the users with K-means to find different, distinct groups.

   
   
   • Given many emails, you want to determine if they are Spam or Non-Spam emails.
   
   
   • Given a database of information about your users, automatically group them into different market segments.

     You can use K-means to cluster the database entries, and each cluster will correspond to a different market segment.

   
   
   • Given sales data from a large number of products in a supermarket, figure out which products tend to form coherent groups (say are frequently purchased together) and thus should be put on the same shelf.

     If you cluster the sales data with K-means, each cluster should correspond to coherent groups of items.

   
   
   • Given sales data from a large number of products in a supermarket, estimate future sales for each of these products.
   
   

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2. Suppose we have three cluster centroids , and .
   Furthermore, we have a training example . After a cluster assignment
   step, what will be?
   
   
   • = 1

     is closest to , So = 1.
     (Calculate Euclidean distance for each centroid and choose the smallest one)

   
   
   • is not assigned
   
   
   • = 2
   
   
   • = 3
   
   

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3. K-means is an iterative algorithm, and two of the following steps are repeatedly carried out in its inner-loop. Which two?
   
   
   • Move the cluster centroids, where the centroids are updated.

     The cluster update is the second step of the K-means loop.

   
   
   • The cluster assignment step, where the parameters are updated.

     This is the correst first step of the K-means loop.

   
   
   • Using the elbow method to choose K.
   
   
   • Feature scaling, to ensure each feature is on a comparable scale to the others.
   
   
   • The cluster centroid assignment step, where each cluster centroid is assigned (by setting ) to the closest training example .
   
   
   • Move each cluster centroid , by setting it to be equal to the closest training example .
   
   
   • Test on the cross-validation set.
   
   
   • Randomly initialize the cluster centroids.
   
   

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4. Suppose you have an unlabeled dataset . You run K-means with 50 different random initializations, and obtain 50 different clusterings of the data.
   
   What is the recommended way for choosing which one of these 50 clusterings to use?
   
   
   • Use the elbow method.
   
   
   • Plot the data and the cluster centroids, and pick the clustering that gives the most “coherent” cluster centroids.
   
   
   • Manually examine the clusterings, and pick the best one.
   
   
   • Compute the distortion function , and pick the one that minimizes this.

     A lower value for the distortion function implies a better clustering, so you should choose the clustering with the smallest value for the distortion function.

   
   
   • The only way to do so is if we also have labels for our data.
   
   
   • Always pick the final (50th) clustering found, since by that time it is more likely to have converged to a good solution.
   
   
   • The answer is ambiguous, and there is no good way of choosing.
   
   
   • For each of the clusterings, compute , and pick the one that minimizes this.

     This function is the distortion function. Since a lower value for the distortion function implies a better clustering, you should choose the clustering with the smallest value for the distortion function.

   
   

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5. Which of the following statements are true? Select all that apply.
   
   
   • On every iteration of K-means, the cost function (the distortion function) should either stay the same or decrease; in particular, it should not increase.

     Both the cluster assignment and cluster update steps decrese the cost / distortion function, so it should never increase after an iteration of K-means.

   
   
   • A good way to initialize K-means is to select K (distinct) examples from the training set and set the cluster centroids equal to these selected examples.

     This is the recommended method of initialization.

   
   
   • K-Means will always give the same results regardless of the initialization of the centroids.
   
   
   • Once an example has been assigned to a particular centroid, it will never be reassigned to another different centroid
   
   
   • For some datasets, the “right” or “correct” value of K (the number of clusters) can be ambiguous, and hard even for a human expert looking carefully at the data to decide.

     In many datasets, different choices of K will give different clusterings which appear quite reasonable. With no labels on the data, we cannot say one is better than the other.

   
   
   • The standard way of initializing K-means is setting to be equal to a vector of zeros.
   
   
   • If we are worried about K-means getting stuck in bad local optima, one way to ameliorate (reduce) this problem is if we try using multiple random initializations.

     Since each run of K-means is independent, multiple runs can find different optima, and some should avoid bad local optima.

   
   
   • Since K-Means is an unsupervised learning algorithm, it cannot overfit the data, and thus it is always better to have as large a number of clusters as is computationally feasible.
   
   

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