Open AccessAn empirical study on shortest path for Graph clustering in Network analysis
Author(s) -
Gourav Kumar,
G. Shobha Latha
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1344/1/012043
Subject(s) - shortest path problem , computer science , average path length , k shortest path routing , cluster analysis , schedule , yen's algorithm , graph theory , longest path problem , clustering coefficient , data mining , graph , theoretical computer science , dijkstra's algorithm , mathematics , artificial intelligence , combinatorics , operating system
Network theory deals with the study of graphs in which nodes connected by branches. It helps to determine the shortest route between two places, time schedule for the activities of a project and minimum cost flow in pipeline networks. In the present paper, we with the existing algorithms based on network theory and finding the shortest path from use the cluster algorithms to find the can be compare one to another and also shortest paths when the clusters are grouping based on cluster analysis the results which we obtain observed that the similarity levels of single, average and complete linkage methods. By applying the existing algorithms based on graph theory can analyze the shortest path from one to another where as clustering provides the shortest path as well as the similarity index for standardized and non-standardized variables.