Frequent subgraph mining in outerplanar graphs
نویسندگان
چکیده
منابع مشابه
Frequent subgraph mining algorithms on weighted graphs
This thesis describes research work undertaken in the field of graph-based knowledge discovery (or graph mining). The objective of the research is to investigate the benefits that the concept of weighted frequent subgraph mining can offer in the context of the graph model based classification. Weighted subgraphs are graphs where some of the vertexes/edges are considered to be more significant t...
متن کاملMining for Unconnected Frequent Graphs with Direct Subgraph Isomorphism Tests
In the paper we propose the algorithm which discovers both connected and unconnected frequent graphs from the graphs set. Our approach is based on depth first search candidate generation and direct execution of subgraph isomorphism test over database. Several search space pruning techniques are also proposed. Due to lack of unconnected graph mining algorithms we compare our algorithm with two g...
متن کاملDiscriminative frequent subgraph mining with optimality guarantees
The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines tw...
متن کاملVisCFSM: Visual, Constraint-Based, Frequent Subgraph Mining
Graphs long have been valued as a pictorial way of representing relationships between entities. Contemporary applications use graphs to model social networks, protein interactions, chemical structures, and a variety of other systems. In many cases, it is useful to detect patterns within graphs. For example, one could be interested in identifying frequently occurring subgraphs, which is known as...
متن کاملFrequent Subgraph Mining Based on Pregel
Graph is an increasingly popular way to model complex data, and the size of single graphs is growing toward massive. Nonetheless, executing graph algorithms efficiently and at scale is surprisingly challenging. As a consequence, distributed programming frameworks have emerged to empower large graph processing. Pregel, as a popular computational model for processing billion-vertex graphs, has be...
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: Data Mining and Knowledge Discovery
سال: 2010
ISSN: 1384-5810,1573-756X
DOI: 10.1007/s10618-009-0162-1