Skip to main content
main-content

Zeitschrift

International Journal of Data Science and Analytics

International Journal of Data Science and Analytics OnlineFirst articles

23.05.2020 | Applications

Identifying Pareto-based solutions for regression subset selection via a feasible solution algorithm

The concept of Pareto optimality has been utilized in fields such as engineering and economics to understand fluid dynamics and consumer behavior. In machine learning contexts, Pareto-optimality has been used to identify tuning parameters that …

21.05.2020 | Regular Paper

Multi-task learning by hierarchical Dirichlet mixture model for sparse failure prediction

Sparsity and noisy labels occur inherently in real-world data. Previously, strong assumptions were made by domain experts to use their experience and expertise to select parameters for their models. Similar approach has been adopted in machine …

20.05.2020 | Regular Paper

Analyzing impact of parental occupation on child’s learning performance: a semantics-driven probabilistic approach

Scientific research on the effect of parent’s socioeconomic status on child’s learning performance is a popular topic from the last century. However, majority of these researches are based on traditional statistical models and involve subjective …

25.04.2020 | Regular Paper Open Access

Clustering of mixed-type data considering concept hierarchies: problem specification and algorithm

Most clustering algorithms have been designed only for pure numerical or pure categorical data sets, while nowadays many applications generate mixed data. It raises the question how to integrate various types of attributes so that one could …

22.04.2020 | Applications

Modelling the electrical energy profile of a batch manufacturing pharmaceutical facility

Sustainable manufacturing practices are a dominating consideration for legacy factories. Major attention is being applied to improving current practices to more sustainable ones. This research provides a case study of a batch manufacturing …

Aktuelle Ausgaben

Über diese Zeitschrift

Data-driven scientific discovery is a key emerging paradigm driving research innovation and industrial development in domains such as business, social sci­ence, the Internet of Things, and cloud computing. The field encompasses the larger ar­eas of data analytics, machine learning, and managing big data, while related new sci­entific chal­lenges range from data capture, creation, storage, search, sharing, analysis, and vis­ualization, to integration across heterogeneous, interdependent complex resources for real-time decision-making, collaboration, and value creation. The journal welcomes experimental and theoretical findings on data science and advanced analytics along with their applications to real-life situations.

Weitere Informationen

Premium Partner

    Bildnachweise