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International Journal of Data Science and Analytics

International Journal of Data Science and Analytics OnlineFirst articles

29-05-2020 | Regular Paper

An improved scheme for determining top-revenue itemsets for placement in retail businesses

Utility mining has been emerging as an important area in data mining. While existing works on utility mining for retail businesses have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly …

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 …

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About this journal

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.

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