Contents
How is cluster distance measured?
For most common clustering software, the default distance measure is the Euclidean distance. Depending on the type of the data and the researcher questions, other dissimilarity measures might be preferred. For example, correlation-based distance is often used in gene expression data analysis.
Why is distance not negative?
Answer: Distance is non-negative for the same reasons as magnitude (it is a measure of length, and the formula squares the values). The distance between two identical vectors is 0, otherwise distance will be positive. Question: Can dot product be negative?
How to calculate the distance of a cluster?
Choose a distance function for clusters – for clusters formed by just one point, D should reduce to d. Start from N clusters, each containing one item. Then, at each iteration: a) using the current matrix of cluster distances, find two closest clusters.
Which is the clustering pattern for complete linkage distance?
We can see that the clustering pattern for complete linkage distance tends to create compact clusters of clusters, while single linkage tends to add one point at a time to the cluster, creating long stringy clusters. As we might expect from our discussion of distances, Euclidean distance and correlation distance produce very different dendrograms.
Is there an objective way to say how many clusters there are?
One of the problems with hierarchical clustering is that there is no objective way to say how many clusters there are. If we cut the single linkage tree at the point shown below, we would say that there are two clusters.
Which is the default distance for hierarchical clustering?
For most common hierarchical clustering software, the default distance measure is the Euclidean distance. This is the square root of the sum of the square differences. However, for gene expression, correlation distance is often used. The distance between two vectors is 0 when they are perfectly correlated.