Add to ORKGThe class of frequent hypergraph mining problems is introduced which includes the frequent graph mining problem class and contains also the frequent itemset mining problem. We study the computational properties of different problems belonging to this class. In particular, besides negative results, we present practically relevant problems that can be solved in incremental-polynomial time. Some of our practical algorithms are obtained by reductions to frequent graph mining and itemset mining problems. Our experimental results in the domain of citation analysis show the potential of the framework on problems that have no natural representation as an ordinary graph
Data mining is a popular research area that has been studied by many researchers and focuses on find...
AbstractThe frequent connected subgraph mining problem, i.e., the problem of listing all connected g...
Mining maximal frequent itemsets is one of the most fundamental problems in data mining. In this pap...
The class of frequent hypergraph mining problems is introduced which includes the frequent graph min...
Abstract. The main practical problem encountered with frequent subgraph search methods is the tens o...
In recent years, the popularity of graph datasets has grown rapidly. Frequent subgraph mining (FSM) ...
Studying the computational complexity of problems is one of the - if not the - fundamental questions...
Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in...
This thesis describes research work undertaken in the field of graph-based knowledge discovery (or g...
In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-g...
We present a survey of the most important algorithms that have been pro- posed in the context of the...
AbstractIn this paper we study the complexity-theoretic aspects of mining maximal frequent patterns,...
In many recent applications, a graph is used to simulate many complex systems, such as social networ...
Large graphs are often used to simulate and model complex systems in variousresearch and application...
Abstract. The exponential number of possible subgraphs makes the problem of frequent subgraph mining...
Data mining is a popular research area that has been studied by many researchers and focuses on find...
AbstractThe frequent connected subgraph mining problem, i.e., the problem of listing all connected g...
Mining maximal frequent itemsets is one of the most fundamental problems in data mining. In this pap...
The class of frequent hypergraph mining problems is introduced which includes the frequent graph min...
Abstract. The main practical problem encountered with frequent subgraph search methods is the tens o...
In recent years, the popularity of graph datasets has grown rapidly. Frequent subgraph mining (FSM) ...
Studying the computational complexity of problems is one of the - if not the - fundamental questions...
Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in...
This thesis describes research work undertaken in the field of graph-based knowledge discovery (or g...
In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-g...
We present a survey of the most important algorithms that have been pro- posed in the context of the...
AbstractIn this paper we study the complexity-theoretic aspects of mining maximal frequent patterns,...
In many recent applications, a graph is used to simulate many complex systems, such as social networ...
Large graphs are often used to simulate and model complex systems in variousresearch and application...
Abstract. The exponential number of possible subgraphs makes the problem of frequent subgraph mining...
Data mining is a popular research area that has been studied by many researchers and focuses on find...
AbstractThe frequent connected subgraph mining problem, i.e., the problem of listing all connected g...
Mining maximal frequent itemsets is one of the most fundamental problems in data mining. In this pap...