Stats 2 Final Chapter 4

Descriptive data-mining

A category of data mining techniques that detect patterns and relationships in the data

dendrogram

A tree diagram used to illustrate the sequence of nested clusters produced by hierarchical clustering is known as a

market basket analysis

An analysis of items frequently co-occurring in transactions (such as purchases) is known

the hypotenuse

If the Euclidean distance were to be represented in a right triangle, which of the following would be considered the distance between two observations

the consequent occurs given that the antecedent occurs

In the theory of association rules in data mining, by confidence we mean an estimated probability that

This is true of bottom-up hierarchical clustering

It starts with each observation in its own cluster and then iteratively combine two most similar clusters

The set of recorded values of variables associated with single entity

Observations refers to the

Cluster Analysis

The data mining method that can be used in the market segmentation to divide consumers into different homogeneous groups is

matching coefficient

The simplest measure of similarity between observations consisting solely of categorical variables is given by

The ability to electronically warehouse data

This reason is responsible for the increase in the use of data-mining techniques in business

Jaccard's coefficient is different from the matching coefficient in that the former

does not count matching zero entries while the latter does.

Complete linkage

measures dissimilarity between two clusters by considering only the two most distant observations in these clusters

Centroid linkage

measures dissimilarity between two clusters by using the distance between the two cluster centroids

The endpoint of a k-means clustering algorithm occurs when

no further changes are observed in cluster structure and number

Single linkage measures dissimilarity between two clusters by considering

only the two closest observations in these clusters

The k-means clustering is the process of

organizing observations into one of k groups based on a measure of similarity.

Average group linkage measures dissimilarity between two clusters by considering

the average distance over all pairs of observations between these clusters

Centroid

the vector of the averages computed for each variable across all cluster observations