عنوان

Canonical labeling to improve compression approach to graph matching

Number

TL49203

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Canonical labeling to improve compression approach to graph matching

General Material Designation

[Thesis]

First Statement of Responsibility

Mohammad R. Islam

Subsequent Statement of Responsibility

Eberle, William

Name of Publisher, Distributor, etc.

Tennessee Technological University

Date of Publication, Distribution, etc.

2016

Specific Material Designation and Extent of Item

63

Text of Note

Committee members: Kosa, Martha; Talbert, Doug

Text of Note

Place of publication: United States, Ann Arbor; ISBN=978-1-369-45317-1

Dissertation or thesis details and type of degree

M.S.

Discipline of degree

Computer Science

Body granting the degree

Tennessee Technological University

Text preceding or following the note

2016

Text of Note

Frequent itemset and sequence mining are known successful data mining approaches for discovering interesting patterns. However, more recently, research efforts have focused on the challenges of discovering frequent patterns in structural data - or data where there is a relationship between entities. One potential solution has involved the use of graph mining, where research has focused on creating efficient and scalable algorithms for frequent subgraph mining. Graph based pattern mining is used in many applications like chemistry, biology, and computer networks, just to name a few. However, with the rise of big data, current research efforts need to focus even more on the issue of scalability in order to be practical in the real-world. In this paper, we introduce a new approach for discovering frequent subgraphs in large datasets using a hybrid approach between two of the more popular subgraph mining algorithms. We empirically evaluate our approach on two different publicly available datasets, one representing chemical compounds and the other representing computer networking. From both of them, our algorithm discovers more meaningful frequent patterns than the other two algorithms.

Computer science

Subject Term

Applied sciences;Data mining;Frequent subgraphs;Gspan;Hybrid;Subdue;Subgraph mining

Youssef, Khaled

Eberle, William

Subdivision

Computer Science

Tennessee Technological University

Call Number

1868414561; 10193263

Electronic name

p

[Thesis]

276903

a

Y