Table of Contents

## Which clustering method can be readily applied to graphs?

There are many applications of graph clustering such as correlation clustering, graph partitioning, community detection, a protein-protein network, etc. Many algorithms of global clustering are described in [6,7,8,9,10,11,12]. …

## Which is a graph clustering algorithm?

The HCS (Highly Connected Subgraphs) clustering algorithm (also known as the HCS algorithm, and other names such as Highly Connected Clusters/Components/Kernels) is an algorithm based on graph connectivity for cluster analysis. This algorithm was published by Erez Hartuv and Ron Shamir in 2000.

## How do you find clusters on a graph?

Edge Betweenness clustering detects clusters in a graph network by progressively removing the edge with the highest betweenness centrality from the graph. Betweenness centrality measures how often a node/edge lies on the shortest path between each pair of nodes in the diagram.

## What is graph based clustering?

Graph clustering is an important subject, and deals with clustering with graphs. Thus in graph clustering, elements within a cluster are connected to each other but have no connection to elements outside that cluster. Also, some recently proposed approaches [2–4] perform clustering directly on graph-based data.

## Why is graph theoretic clustering necessary in data mining?

It thus represents data in such a way that it is easier to find meaningful clusters on this new representation. It is especially useful in complex datasets where traditional clustering methods would fail to find groupings.

## What is a scalable clustering algorithm?

In this paper we propose an algorithm to cluster large-scale data sets without clustering all the data at a time. Data is randomly divided into almost equal size disjoint subsets. We then cluster each subset using the hard-k means or fuzzy k-means algorithm.

## Why is graph theoretic clustering necessary?

## What is scalable clustering algorithm?

In this paper we propose an algorithm to cluster large- scale data sets without clustering all the data at a time. Data is randomly divided into almost equal size disjoint subsets. We then cluster each subset using the hard-k means or fuzzy k-means algorithm. The centroids of subsets form an ensemble.

## How is graph theory used in data mining?

Graph-based data mining represents a collection of techniques for mining the relational aspects of data represented as a graph. Two major approaches to graph based data mining are frequent sub graph mining and graph- based relational learning. The need for mining structured data has increased rapidly.

## Why is cluster computing highly scalable?

In terms of scalability, clusters provide this in their ability to add nodes horizontally. This means that more computers may be added to the cluster, to improve its performance, redundancy and fault tolerance.