In many modern applications, the generated data is a dynamic network. These networks are
graphs that change over time by a sequence of update operations (node addition, node …

In recent years, advanced machine learning and artificial intelligence techniques have
gained popularity due to their ability to solve problems across various domains with high …

We consider a new tree mining problem that aims to discover restrictedly embedded subtree
patterns from a set of rooted labeled unordered trees. We study the properties of a canonical …

The set of all frequent patterns that are extracted from a single network can be huge. A
technique recently proposed for obtaining a compact, informative and useful set of patterns …

Mining frequent tree patterns has many applications in different areas such as XML data,
bioinformatics and World Wide Web. The crucial step in frequent pattern mining is frequency …

We propose unsupervised representation learning and feature extraction from dendrograms.
The commonly used Minimax distance measures correspond to building a dendrogram with …

Frequent tree patterns have many applications in different domains such as XML document
mining, user web log analysis, network routing and bioinformatics. In this paper, we first …

From early days, a key and controversial question inside the artificial intelligence community
was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines …

A well-known problem in data science and machine learning is linear regression, which is
recently extended to dynamic graphs. Existing exact algorithms for updating solutions of …

A well-studied problem in data mining is large itemset mining. To address this problem over
very large datasets, several approximate algorithms have been introduced, where an …