Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets,...

The mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences. A method, called GTRACE, has been proposed to mine frequent patterns from graph sequences under the assumption that changes in graphs are gradual. Although GTRACE mines the frequent patterns efficiently, it sti...

A program of study can be viewed as a knowledge graph consisting of learning units and relationships between them. Such a knowledge graph provides the core data structure for organizing and navigating learning experiences. We address two issues in this talk. First, given a knowledge graph, how can we use data mining to identify and correct deficiencies in a knowledge graph. Second, how can we u...

We report a tool called Akshaya, which implements a framework to mine four types of “general knowledge semantics” (analytical semantics) from unstructured text. The semantics being mined are semantic siblings, topical anchors, topic expansion and topical markers. The framework provides options to embed more such general knowledge semantic mining algorithms into it. We use a term co-occurrence g...