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International Journal of Data Science and Analytics OnlineFirst articles

15-06-2021 | Regular Paper

CPI-model-based analysis of sparse k-means clustering algorithms

Standard k-means clustering algorithms have been widely used to solve the partitioning problems of a given data set into k disjoint subsets. When a data set is large-scale and high-dimensional sparse, such as text data with a bag-of-words …

09-06-2021 | Regular Paper

Incremental learning strategies for credit cards fraud detection

Every second, thousands of credit or debit card transactions are processed in financial institutions. This extensive amount of data and its sequential nature make the problem of fraud detection particularly challenging. Most analytical strategies …

08-06-2021 | Regular Paper Open Access

Conventional displays of structures in data compared with interactive projection-based clustering (IPBC)

Clustering is an important task in knowledge discovery with the goal to identify structures of similar data points in a dataset. Here, the focus lies on methods that use a human-in-the-loop, i.e., incorporate user decisions into the clustering …

31-05-2021 | Regular Paper Open Access

Analyzing the impact of missing values and selection bias on fairness

Algorithmic decision making is becoming more prevalent, increasingly impacting people’s daily lives. Recently, discussions have been emerging about the fairness of decisions made by machines. Researchers have proposed different approaches for …

25-05-2021 | Regular Paper

An incremental framework to extract coverage patterns for dynamic databases

Pattern mining is an important task of data mining and involves the extraction of interesting associations from large transactional databases. Typically, a given transactional database D gets updated due to the addition and deletion of …

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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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