So far, we've worked with a non-hierarchical clustering algorithm, k-means clustering. K-means works by taking a set parameter that tells it how many clusters we think exist in the data, and then uses ...

Understanding Hierarchical Clustering So far, we've worked with a non-hierarchical clustering algorithm, k-means clustering. K-means works by taking a set parameter that tells it how many clusters we ...

Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, full-sib families and selfing lines) have recently received much attention in population ...

Abstract: An algorithm was developed to characterize, compare, and analyze eye movement sequences that occur during visual tracking of multiple moving targets. When individuals perform a task ...

Abstract: This research uses agglomerative hierarchical clustering to classify credit card users into behaviorally different segments, facilitating data-driven marketing. Examining a large database of ...

Clustering data is the process of grouping items so that items in a group (cluster) are similar and items in different groups are dissimilar. After data has been clustered, the results can be analyzed ...