Which Of The Following Statements Is True Of Public Services?

▸ Unsupervised Learning :

For which of the following tasks might K-means clustering exist a suitable algorithm
Select all that apply.
- Given a set of news articles from many dissimilar news websites, notice out what are the main topics covered.
  K-ways can cluster the articles and then we tin 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 unlike groups of users be.
  We tin cluster the users with K-means to find dissimilar, distinct groups.
- Given many emails, you want to determine if they are Spam or Not-Spam emails.
- Given a database of information most your users, automatically group them into dissimilar market place segments.
  You can use K-means to cluster the database entries, and each cluster will correspond to a different marketplace segment.
- Given sales data from a big 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 aforementioned shelf.
  If you cluster the sales data with K-means, each cluster should stand for to coherent groups of items.
- Given sales data from a large number of products in a supermarket, estimate futurity sales for each of these products.

Suppose we have three cluster centroids $\mu_1 = \begin{bmatrix} 1\\ 2 \end{bmatrix}$ , $\mu_2 = \begin{bmatrix} -3\\ 0 \end{bmatrix}$ and $\mu_3 = \begin{bmatrix} 4\\ 2 \end{bmatrix}$ .
Furthermore, nosotros take a training instance $x^{(i)} = \begin{bmatrix} -1\\ 2 \end{bmatrix}$ . After a cluster consignment
stride, what will $C^{(i)}$ be?

Yard-ways is an iterative algorithm, and two of the following steps are repeatedly carried out in its inner-loop. Which 2?

Suppose you accept an unlabeled dataset $\{x^{(1)}, ... , x^{(m)}\}$ . You lot run One thousand-means with 50 unlike random initializations, and obtain fifty different clusterings of the data.
What is the recommended way for choosing which ane of these fifty clusterings to utilize?

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Which of the following statements are true? Select all that apply.
- On every iteration of K-means, the cost office $J(C^{(1)}, ... , C^{(m)}, \mu_1, ... , \mu_k)$ (the distortion function) should either stay the same or decrease; in item, it should not increase.
  Both the cluster assignment and cluster update steps decrese the toll / distortion function, so it should never increase after an iteration of K-ways.
- A good way to initialize Grand-means is to select K (distinct) examples from the training set and ready the cluster centroids equal to these selected examples.
  This is the recommended method of initialization.
- Grand-Means volition always requite the aforementioned results regardless of the initialization of the centroids.
- Once an example has been assigned to a detail centroid, it volition never exist reassigned to another unlike centroid
- For some datasets, the "right" or "correct" value of Thou (the number of clusters) tin 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 unlike clusterings which announced quite reasonable. With no labels on the data, we cannot say one is better than the other.
- The standard fashion of initializing K-means is setting $\mu_1 = ... = \mu_k$ to exist equal to a vector of zeros.
- If nosotros are worried about G-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 G-means is independent, multiple runs can find dissimilar optima, and some should avoid bad local optima.
- Since Chiliad-Means is an unsupervised learning algorithm, it cannot overfit the data, and thus it is always better to take as large a number of clusters equally is computationally viable.

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