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Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery is a peer-reviewed journal published by Wiley, covering Data Mining Algorithms and Applications, Data Management and Algorithms, Diverse Scientific and Economic Studies.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery 12 (3): 45–58.
Formats any DOI in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery style. No sign-up. |
| Publishes research in | Data Mining Algorithms and Applications Data Management and Algorithms Diverse Scientific and Economic Studies Human auditory perception and evaluation Machine Learning and Data Classification |
| ISSN | 1942-4787 |
| Citation impact (2-yr) | 6.38 |
| h-index | 95 |
| i10-index | 386 |
| Total citations | 47,367 |
| Article processing charge | $4,070 |
| Top institutions publishing here | Universidade do Porto |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and discusses current challenges and trends in the field.…
Abstract Classification and regression trees are machine‐learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take…
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state‐of‐the‐art prediction results. Along with the success of deep learning in many application domains, deep learning is also used in sentiment analysis in recent years. This paper gives an overview of deep…
Abstract We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self‐organizing maps, and mixture models. We review grid‐based clustering, focusing on hierarchical density‐based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be…
Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI…
