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Can K means algorithm be used for community detection?
Community detection is similar to clustering analysis in many aspects, so the state-of-the-art clustering algorithms such as K-means and DBSCAN can be easily altered to detect communities in networks[17-19].
What is the difference between community detection and clustering?
Often clustering and community detection are used interchangeably in the literature. Clustering mostly focuses on a single modality, e.g., using node attributes to group network objects, whereas community detection focuses on network structure as a function of connectivity involving social interaction.
Is K-means the best clustering algorithm?
Kmeans clustering is one of the most popular clustering algorithms and usually the first thing practitioners apply when solving clustering tasks to get an idea of the structure of the dataset. The goal of kmeans is to group data points into distinct non-overlapping subgroups.
What is elbow Method K-means?
Elbow Method It is an empirical method to find out the best value of k. it picks up the range of values and takes the best among them. It calculates the sum of the square of the points and calculates the average distance. When the value of k is 1, the within-cluster sum of the square will be high.
What is a community graph?
Qualitatively, a community is defined as a subset of nodes within the graph such that connections between the nodes are denser than connections with the rest of the network. In this way nodes are grouped into larger and larger communities, and the tree is built up to the root, which represents the whole network.
What is better than K means clustering?
Gaussian Mixture Models (GMMs) give us more flexibility than K-Means. Taking an example in two dimensions, this means that the clusters can take any kind of elliptical shape (since we have a standard deviation in both the x and y directions). Thus, each Gaussian distribution is assigned to a single cluster.