SUBDUE is a graph-based knowledge discovery system that finds structural, relational patterns in data representing entities and relationships. SUBDUE represents data using a labeled, directed graph in which entities are represented by labeled vertices or subgraphs, and relationships are represented by labeled edges between the entities. SUBDUE uses the minimum description length (MDL) principle to identify patterns that minimize the number of bits needed to describe the input graph after being compressed by the pattern. SUBDUE can perform several learning tasks, including unsupervised learning, supervised learning, clustering and graph grammar learning.
The workshop aims to discuss key issues and practices of semantic mining. Thanks to the initiatives of the Linked Open Data and robust techniques for semantic annotation of Web, social, and sensor data, more semantic data is available. Many research efforts have been directed toward demonstrating semantic techniques to analyze and mine this growing resource. The workshop will provide a cross-disciplinary forum for researchers to showcase their innovation and efforts, and to further enhance existing bounds and create new connections among different communities. Here we solicit contributions on researches and practices of mining data semantics including theory, algorithms, and applications from computer science, life science, healthcare and other domains.
At the highest level of description, this book is about data mining. However,
it focuses on data mining of very large amounts of data, that is, data so large
it does not fit in main memory. Because of the emphasis on size, many of our
examples are about the Web or data derived from the Web. Further, the book
takes an algorithmic point of view: data mining is about applying algorithms
to data, rather than using data to “train” a machine-learning engine of some