How graph theory is used in data mining?

Graph theory is used to describe a set of objects, or nodes, and the relationships, or edges, between the nodes. In computational chemistry, nodes are used to represent the atoms in chemical structures.

How is graph theory used in medicine?

Within the fields of Biology and Medicine, potential applications of network analysis by using graph theory include identifying drug targets, determining the role of proteins or genes of unknown function. There are several biological domains where graph theory techniques are applied for knowledge extraction from data.

What is graph theory used for?

Graph Theory is ultimately the study of relationships. Given a set of nodes & connections, which can abstract anything from city layouts to computer data, graph theory provides a helpful tool to quantify & simplify the many moving parts of dynamic systems.

What is practical application of graph mining?

Practical Graph Mining with R presents a “do-it-yourself” approach to extracting interesting patterns from graph data. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems.

How is graph theory used in data mining?

Data mining is one of those fields where concepts of graph theory have been applied to a large extent. Data mining (Han et al, 2006) is the subject which deals in extraction of knowledge from the available da ta. Various algorithms are applied which help in the analysis and establishment of relationship between the entities.

How to find frequent substructures in graph mining?

Discovery of Frequent Substructures  Step 1: Generate frequent sub-structure candidates  Step 2: Check for frequency of each candidate  Involves sub-graph isomorphism test which is computationally expensive  Approaches  Apriori –based approach  Pattern Growth approach 4 5.

How does apriori approach to graph mining work?

Apriori Approach  FSG (Frequent Sub-graph mining)  Edge-based Candidate generation – increases by one-edge at a time  Two size k patterns are merged iff they share the same subgraph having k-1 edges (core)  New candidate – has core and the two additional edges 7 8.