ACM Transactions on Knowledge Discovery from Data

In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T, and a budget L, we find a set of billboards within the ...

We enhance the mobile sequential recommendation (MSR) model and address some critical issues in existing formulations by proposing three new forms of the MSR from a multi-user perspective. The multi-user MSR (MMSR) model searches optimal routes for ...

The goal of metric learning is to learn a good distance metric that can capture the relationships among instances, and its importance has long been recognized in many fields. An implicit assumption in the traditional settings of metric learning is that ...

Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and ...

A huge object collection in high-dimensional space can often be clustered in more than one way, for instance, objects could be clustered by their shape or alternatively by their color. Each grouping represents a different view of the dataset. The new ...

We present MiSoSouP, a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different popular interestingness measures, from a random sample of a transactional dataset. We describe a new ...

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g., the ...

The G-Skyline (GSky) query is a powerful tool to analyze optimal groups in decision support. Compared with other group skyline queries, it releases users from providing an aggregate function. Besides, it can get much comprehensive results without ...

The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which ...

Diversity maximization is a fundamental problem in web search and data mining. For a given dataset S of n elements, the problem requires to determine a subset of S containing k≪n “representatives” which maximize some diversity function expressed in ...

Given a labeled dataset that contains a rare (or minority) class containing of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? The ...

Recently, a lot of research work has been proposed in different domains to detect outliers and analyze the outlierness of outliers for relational data. However, while sequence data is ubiquitous in real life, analyzing the outlierness for sequence data ...

Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles based on structural similarity and ...