Which of the following are important limitations of K-means Clustering? The user has to specify K (the number of clusters) to the algorithm K-means Clustering is only good at detecting roughly spherical-shaped clusters and cannot detect other patterns like elongated clusters K-means Clustering is quite sensitive to any outliers present in the data All of the above
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Which of the following statements is/are true in the case of k-means clustering? 1. For using k-means clustering on the data, it requires the number of clusters to be specified. 2. The value of k can take any value in the range of 1 to n (number of data points). 3. The k-means algorithm does clustering based on the distance between the data points and the cluster centroids. All the statements are true (2) & (3) Only (1) (1) & (3)
Lien L.
20) K-Means algorithm: Select the correct statement among the following: a) The results of the k-means algorithm get impacted by outliers and the range of the attributes. b) K-means clustering automatically selects the most optimum value of k c) The clusters formed by the k-means algorithm do not depend on the initial selection of cluster centers. d) the k-means algorithm can be applied to both categorical and numerical variables. 21) Which of the following options are prerequisites for the k-means algorithm: a) initial centers should be very close to each other b) Choice of number of clusters c) Choice of initial centroids 22) K-Means algorithm: Arrange the steps of the k-means algorithm in the order in which they occur: 1. Randomly selecting the cluster centroids 2. Updating the cluster centroids iteratively 3. Assigning the cluster points to their nearest center a) 1-3-2 a) 2-1-3 b) 1-2-3 23) Which of the following are the applications of clustering? a) Identifying consumer segments and their properties to position products appropriately b) Identifying patterns of crime in different regions of a city and managing police enforcement based on frequency and type of crime c) Looking at social media behavior to find out the types of online communities that exist d) All of the above 24) Select the appropriate option which describes the Complete Linkage method. a) In complete linkage hierarchical clustering, the inter-cluster distance is defined as the shortest distance between two points (one point in each cluster). b) In complete linkage hierarchical clustering, the inter-cluster distance is defined as the longest distance between two points (one point in each cluster) c) In complete linkage hierarchical clustering, the inter-cluster distance is defined as the average distance between two points (one point in each cluster)
Madhur L.
The choice of k, which is the number of clusters to partition a set of data in k-means clustering, depends on the size of the dataset. It can be set by a subject matter expert or constraints of the business. It should always be as large as your computer system can handle. It has a maximum of 5.
Aparna S.
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