Cluster Analysis Considerations

An object with an extremely large value may substantially distort the distribution of the data.

The k-means algorithm uses the mean value of the objects in a cluster as a reference point, whereas the k-medoids algorithm uses the most centrally located object (medoid) in a cluster.

To find the optimal set of medoids that minimize the total distance of the resulting clustering.

<p>It does not scale well for large data sets due to the computational complexity.</p> Signup and view all the answers

<p>To find representative objects (medoids) in clusters.</p> Signup and view all the answers

<p>Learning by observations without predefined classes.</p> Signup and view all the answers

<p>High intra-class similarity and low inter-class similarity.</p> Signup and view all the answers

<p>To express similarity between data objects.</p> Signup and view all the answers

<p>To account for differences in application and data semantics.</p> Signup and view all the answers

<p>It measures the 'goodness' of a cluster.</p> Signup and view all the answers

<p>K-means is an unsupervised learning method, whereas supervised learning involves predefined classes.</p> Signup and view all the answers

<p>Finding similarities between data objects and grouping them into clusters.</p> Signup and view all the answers

<p>To determine the similarity or dissimilarity between data objects.</p> Signup and view all the answers

<p>The quality of clustering depends on the intra-class similarity and inter-class similarity of the clusters.</p> Signup and view all the answers

<p>Clustering helps to identify patterns or relationships in data by grouping similar objects together.</p> Signup and view all the answers