Data Mining and Knowledge Discovery

Knowledge graphs have shown increasing importance in broad applications such as question answering, web search, and recommendation systems. The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs ...

Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing projection methods for data visualization use predefined criteria to choose the representation of data. There is a lack ...

Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data distribution may ...

Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the ...

We consider the class of linear predictors over all logical conjunctions of binary attributes, which we refer to as the class of combinatorial binary models (CBMs) in this paper. CBMs are of high knowledge interpretability but naïve learning of ...${}_{\mathrm{}}$${}_{\mathrm{}}$

Conventional general-purpose distance-based similarity measures, such as Minkowski distance (also known as ${\ell }_p$-norm with $p>0$), are data-independent and sensitive to units or scales of measurement. There are existing general-purpose data-dependent ...${}_{}$$\mathrm{}$${}_{}$$\mathrm{}$${}_{\mathrm{}}$${}_{}$

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the ...$$

Rapid urbanization has made the estimation of the human mobility flows a substantial task for transportation and urban planners. Worker and student mobility flows are among the most weekly regular displacements and consequently generate road ...

In recent years, many new ensemble-based time series classification (TSC) algorithms have been proposed. Each of them is significantly more accurate than their predecessors. The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-...