Subject: Concept of Clustering

$1)$ Why do we need evaluation measures for cluster algorithms?

$2)$ What is cluster separation and cluster cohesion?

$3)$ Why is SSE not necessarily a good measure of cluster quality?

$4)$ What is a silhouette? What is its range? Is it a characteristic of a cluster, a variable, or a data value?

$5)$ How do we interpret a silhouette value?

$6)$ Explain how silhouette accounts for both separation and cohesion.

$7)$ How is average silhouette interpreted?

$8)$ When will a data value have a perfect silhouette value? What is this value?

$9)$ Describe what a silhouette plot is$.$

$10)$ Should the analyst always choose the cluster solution with the better mean silhouette value? Explain.

$11)$ Explain how the pseudo$-$F statistic accounts for both separation and cohesion.

$12)$ Why does the pseudo$-$F statistic have the word pseudo in its name?

$13)$ Explain how we can use the pseudo$-$F statistic to select the optimal number of clusters.

$14)$ True or false: The best clustering model is the one with the largest value of pseudo$-$F$.$ Explain.

$15)$ What is our cluster validation methodology?

$16)$ Why might statistical hypothesis tests not be very helpful for big data applications?