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▸ Unsupervised Learning :

1. For which of the following tasks might 1000-means clustering exist a suitable algorithm
   Select all that apply.
   
   
   • Given a set up of news articles from many dissimilar news websites, find out what are the master topics covered.

     K-ways tin can cluster the articles and and so we can audit them or utilize other methods to infer what topic each cluster represents
     

   
   
   • Given historical weather records, predict if tomorrow'due south 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 Thou-means to find different, singled-out groups.

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

     You tin can apply K-ways to cluster the database entries, and each cluster volition represent to a dissimilar market segment.

   
   
   • Given sales information from a large number of products in a supermarket, figure out which products tend to form coherent groups (say are oft 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 hereafter sales for each of these products.
   
   

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2. Suppose nosotros have three cluster centroids , and .
   Furthermore, nosotros have a training instance . After a cluster consignment
   step, what volition be?

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3. 1000-means is an iterative algorithm, and two of the following steps are repeatedly carried out in its inner-loop. Which two?

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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 fifty clusterings to use?

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5. Which of the post-obit statements are truthful? Select all that apply.
   
   
   • On every iteration of One thousand-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 price / distortion function, so it should never increase after an iteration of Thou-means.

   
   
   • A good mode to initialize 1000-means is to select Thousand (singled-out) examples from the training set and set the cluster centroids equal to these selected examples.

     This is the recommended method of initialization.

   
   
   • 1000-Means will always give the same results regardless of the initialization of the centroids.
   
   
   • Once an example has been assigned to a particular centroid, information technology will never be reassigned to another dissimilar centroid
   
   
   • For some datasets, the "right" or "correct" value of Yard (the number of clusters) tin can exist cryptic, and difficult fifty-fifty for a human being adept looking carefully at the data to decide.

     In many datasets, dissimilar choices of G will give different clusterings which appear quite reasonable. With no labels on the data, we cannot say 1 is improve than the other.

   
   
   • The standard style of initializing Thou-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 mode to ameliorate (reduce) this problem is if nosotros effort using multiple random initializations.

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

   
   
   • Since K-Means is an unsupervised learning algorithm, information technology 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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