Contents
What do you need to know about graph based clustering?
Graph-based Clustering ●Transform the data into a graph representation –Vertices are the data points to be clustered –Edges are weighted based on similarity between data points Þ Graph partitioning Each connected component is a cluster Clustering as Graph Partitioning ●Two things needed:
What is ratio cut in graph based clustering?
Cut = 1 Ratio cut = 1/1 + 1/5 = 1.2 Normalized cut = 1/1 + 1/9 = 1.11 Cut = 2 Ratio cut = 1/3 + 1/3 = 0.67 Normalized cut = 1/5 + 1/5 = 0.2 1 1 1 1 v 1 v 3 v 2 v 5 v 4
How is similarity determined in hierarchical agglomerative clustering?
Hierarchical Agglomerative Clustering (HAC) Assumes a similarity function for determining the similarity of two clusters. Starts with all instances in a separate cluster and then repeatedly joins the two clusters that are most similar until there is only one cluster. The history of merging forms a binary tree or hierarchy.
Which is a common trait of clustering algorithms?
A common trait of the more established clustering algorithms such as K -Means and HCA is their tendency to focus mainly on the bulk features of the data which causes minor features to be attributed to larger clusters.
Shared Nearest Neighbor Betweenness Centrality Based Highly Connected Components Maximal Clique Enumeration Kernel k-means Application Outline Introduction to Clustering Introduction to Graph Clustering Algorithms for Graph Clustering k-Spanning Tree Shared Nearest Neighbor Betweenness Centrality Based Highly Connected Components
Which is the best definition of cluster analysis?
Shared Nearest Neighbor Betweenness Centrality Based Highly Connected Components Maximal Clique Enumeration Kernel k-means Application What is Cluster Analysis? The process of dividing a set of input data into possibly overlapping, subsets, where elements in each subset are considered related by some similarity measure