Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases is expensive due to the complexity of graph data. In this paper, we study the problem of mining top-k correlative subgraphs in the database, which share similar occurrence distributions with a given query graph. The se...

Itemset mining and graph mining have attracted considerable attention in the field of data mining, since they have many important applications in various areas such as biology, marketing, and social network analysis. However, most existing studies focus only on either itemset mining or graph mining, and only a few studies have addressed a combination of both. In this paper, we introduce a new p...

Many research areas have begun representing massive data sets as very large graphs. Thus, graph mining has been an active research area in recent years. Most of the graph mining research focuses on mining unweighted graphs. However, weighted graphs are actually more common. The weight on an edge may represent the likelihood or logarithmic transformation of likelihood of the existence of the edg...

Graph has become increasingly important in modeling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. Different from the existing methods, our approach, called VFM (Vertex to Frequent Feature Mapping), makes use of vertices and decision...

Given a role-explicit topic, we firstly determine its kernel-object, then we construct the corresponding modifier graph (Step-1). We perform graph clustering on the induced modifier graph using the algorithm by Noack [4]. Each modifier cluster is viewed as a representation of a particular subtopic. Corresponding to each modifier cluster, we generate the clusters of subtopic strings based on the...

The localisation of defects in computer programmes is essential in software engineering and is important in domain-specific data mining. Existing techniques which build on call-graph mining localise defects well, but do not scale for large software projects. This paper presents a hierarchical approach with good scalability characteristics. It makes use of novel call-graph representations, frequ...

With the expansion of e-commerce and mobile-based commerce, the role of web user on World Wide Web has become pivotal enough to warrant studies to further understand the user’s intent, navigation patterns on websites and usage needs. Using web logs on the servers hosting websites, site owners and in turn companies, can extract information to better understand and predict user’s needs, tailoring...

This paper proposes an improved approach to mine strong association rules from an association graph, called graph based association rule mining (GBAR) method, where the association for each frequent itemset is represented by a sub-graph, then all sub-graphs are merged to determine association rules with high confidence and eliminate weak rules, the proposed graph based technique is self-motivat...