Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis
نویسندگان
چکیده
منابع مشابه
Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis
The Apriori-based graph mining method is an extension of the Apriori algorithm for association rule mining. It constructs a lattice of graph nodes, in which a node at the k-th level of the lattice has k vertices and the number of supporting instances exceeds a user-specified minimum support. The method can devise a rule “IF subgraph Ga is in transaction G, the union of subgraphs Ga∪Gb is also c...
متن کاملUsing a Hash-Based Method for Apriori-Based Graph Mining
The problem of discovering frequent subgraphs of graph data can be solved by constructing a candidate set of subgraphs first, and then, identifying within this candidate set those subgraphs that meet the frequent subgraph requirement. In Apriori-based graph mining, to determine candidate subgraphs from a huge number of generated adjacency matrices is usually the dominating factor for the overal...
متن کاملAn Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
This paper proposes a novel approach named AGM to e ciently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the arti cial simulatio...
متن کاملGraph-Based 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 graphbased data mining are frequent subgraph mining and graph-based relational learning. This article will focus on one particular approach embodied in the Subdue system, along with recent advances in graph-based supervised learning, graph-based...
متن کاملAnalysis on Parallelization of Apriori Algorithm in Data Mining
Many algorithms are designed to analyse volumes of data automatically in an efficient way so that the users don’t have to look through that massive amount of data manually for generating various association rules among them. Apriori algorithm, which is the most famous and frequently used data mining algorithm. Our main focus is to parallelize the Apriori algorithm in such a new way that when we...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computer Aided Chemistry
سال: 2001
ISSN: 1345-8647
DOI: 10.2751/jcac.2.87