What is Subgraph mining?
What is Subgraph mining?
Abstract. Frequent subgraph mining (FSM) is defined as finding all the subgraphs in a given graph that appear more number of times than a given value. It consists of two steps broadly, first is generating a candidate subgraph and second is calculating support of that subgraph.
What is graph mining used for?
Graph Mining is the set of tools and techniques used to (a) analyze the properties of real-world graphs, (b) predict how the structure and properties of a given graph might affect some application, and (c) develop models that can generate realistic graphs that match the patterns found in real-world graphs of interest.
How do you identify subgraphs in a graph in data mining?
Frequent Subgraph Mining
- Steps in finding frequent subgraphs:
- The Apriori-based approach: The approach to find the frequent graphs begin from the graph with a small size.
- Algorithm:
- The Pattern- growth approach: This pattern-growth approach can use both BFS and DFS(Depth First Search).
- Algorithm:
What is graph pattern mining?
Graph pattern mining is the mining of frequent subgraphs (also called (sub)graph patterns) in one or a set of graphs. Methods for mining graph patterns can be categorized into Apriori-based and pattern growth–based approaches.
Which of the following is a method for mining frequent subgraphs?
gSpan gSpan [10] is one of the most well known algorithms for frequent subgraph mining.
What kind of patterns can be mined in data mining?
As mentioned earlier, generally speaking, data mining tasks and patterns can be classified into three main categories: prediction, association, and clustering.
What are the types of pattern mining?
What are the technologies used in data mining?
10 Key Data Mining Techniques and How Businesses Use Them
- Clustering.
- Association.
- Data Cleaning.
- Data Visualization.
- Classification.
- Machine Learning.
- Prediction.
- Neural Networks.
