Academic Journal
Q1Data Mining and Knowledge Discovery
About Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery is a scholarly journal published by Springer Netherlands. SCImago 2025 lists it in Q1, with an SJR of 1.2 and H-index of 131.
Coverage: 1997-2026. Research categories: Computer Networks and Communications (Q1); Computer Science Applications (Q1); Information Systems (Q1).
Source-backed journal facts
Topics in published research
Data Mining Algorithms and Applications; Time Series Analysis and Forecasting; Data Management and Algorithms; Anomaly Detection Techniques and Applications; Complex Network Analysis Techniques; Advanced Graph Neural Networks.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Reported open-access list prices
3,190.00 USD; 2,590.00 EUR; 2,290.00 GBP
APC list prices reported by OpenAlex, which obtains this information from DOAJ. Confirm current charges, taxes, waivers and eligibility with the publisher; this is not a fee quotation.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-02. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
Quartile, SJR and the listed SCImago H-index use the 2025 imported SCImago dataset. A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
A novel graph-based patient embedding method for the diagnosis of Alzheimer’s disease from microRNA expression data
Veronica Buttaro, Antonio Pellicani, Gianvito Pio, Domenica D’Elia et al.
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Qiming Yang, Wei Wei, Ruizhi Zhang, You Lv et al.
2026-12 · DOI: 10.1007/s10618-026-01277-wEvolution of sentiment analysis toward aspect-based sentiment analysis: a scientometric analysis of research trends, knowledge structures, and emerging technologies (2015–2025)
Neha Goyal, Rajiv Bansal
2026-12 · DOI: 10.1007/s10618-026-01273-0Sparse oblique rule boosting for simpler additive rule ensembles
Shahrzad Behzadimanesh, Pierre Le Bodic, Geoffrey I. Webb, Mario Boley et al.
2026-12 · DOI: 10.1007/s10618-026-01241-8One-step tensor low-frequency learning for incomplete multi-view clustering
Lisha Zhao, Hongwei Ge, Shuzhi Su
2026-12 · DOI: 10.1007/s10618-026-01263-2Minimum length estimation in longest frequent itemsets mining
William Kery Branston Ndemaze, Hervé Maradona Nana Kouassi, Arnauld Nzegha Fountsop, Edith Belise Kenmogne et al.
2026-12 · DOI: 10.1007/s10618-026-01270-3DiaSeg: diagonal segment extraction from DTW paths for interpretable gait analysis
Tresor Y. Koffi, Amel Hidouri, Corentin Legrand, Aurélie Bertaux et al.
2026-12 · DOI: 10.1007/s10618-026-01276-xMultimodal knowledge graph completion method for addressing imbalance of modal information
Ronghua Tian, Hong Yu, Yaogang Geng, Xiaoling Wang et al.
2026-12 · DOI: 10.1007/s10618-026-01272-1Focused PU learning from imbalanced data
Elias Zavitsanos, Georgios Paliouras
2026-12 · DOI: 10.1007/s10618-026-01264-1Hierarchical graph networks for breast cancer subtype classification
Antonio M. Rinaldi, Cristiano Russo, Cristian Tommasino
2026-12 · DOI: 10.1007/s10618-026-01259-yGraph-enhanced bidirectional GRU for anomaly detection in power generation environments
Dongwook Kwon, Youngshin Kang, Jiwoon Lee, Yusang Nam et al.
2026-12 · DOI: 10.1007/s10618-026-01262-3When, how long and how much? Interpretable neural networks for time series regression by learning to mask and aggregate
Florent Forest, Amaury Wei, Olga Fink
2026-12 · DOI: 10.1007/s10618-026-01267-yPoisson subspace clustering: focusing on the essentials in count data
Collin Leiber, Kai Puolamäki, Heikki Mannila
2026-12 · DOI: 10.1007/s10618-026-01230-xDNetEx: FDR-controlled differential network analysis for knowledge discovery from graphs
Mojtaba Nikahd, Ala Emrani, Seyed Abolfazl Motahari
2026-12 · DOI: 10.1007/s10618-026-01245-4Large-scale structured subspace clustering
Mo Chen, Xuesong Yin, Qi Huang, Jianhao Ding et al.
2026-12 · DOI: 10.1007/s10618-026-01268-xTime series classification with random convolution kernels: pooling operators and input representations matter
Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti et al.
2026-12 · DOI: 10.1007/s10618-026-01269-wExplainable course recommendation with knowledge graphs: a comparative audit of diverse modeling paradigms
Neda Afreen, Ludovico Boratto, Gianni Fenu, Francesca Maridina Malloci et al.
2026-12 · DOI: 10.1007/s10618-026-01261-4MOUFLON: multi-group modularity-based fairness-aware community detection
Georgios Panayiotou, Anand Mathew Muthukulam Simon, Matteo Magnani, Ece Calikus et al.
2026-12 · DOI: 10.1007/s10618-026-01260-5Clustering of count data using nested multinomial Dirichlet finite mixture model and its extensions
Fares Alkhawaja, Manar Amayri, Nizar Bouguila
2026-12 · DOI: 10.1007/s10618-026-01250-7Direction-aware multi-label feature selection via paired signed-deviation lifting
Filippo Casu, Andrea Lagorio, Giuseppe A. Trunfio
2026-12 · DOI: 10.1007/s10618-026-01265-0Reviews
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October 4, 2026 at 8:55 pm
September 25, 2026