Data Mining and Knowledge Discovery
Overview
Data Mining and Knowledge Discovery is a reputable journal that focuses on the exploration and extraction of valuable insights from large datasets. It covers various techniques, algorithms, and methodologies used in data mining, machine learning, and knowledge discovery. The journal aims to promote advancements in these fields and their practical applications across diverse domains, fostering innovation and informed decision-making.
Details
Details
Abbr.
Data Min Knowl Discov
Start
1990s
End
Continuing
Frequency
Bimonthly, 2006-
p-ISSN
1384-5810
e-ISSN
1573-756X
Country
United States
Language
English
Metrics
Metrics
h-index / Ranks: 2345
117
SJR / Ranks: 1416
1813
CiteScore / Ranks: 990
11.80
JIF / Ranks: 1690
4.8
Recent Articles
1.
Moradinasab N, Sharma S, Bar-Yoseph R, Radom-Aizik S, Bilchick K, Cooper D, et al.
Data Min Knowl Discov
. 2025 Feb;
38(3):1493-1519.
PMID: 39949582
The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based approaches have achieved promising performance over traditional methods...
2.
Bernardini G, Liu C, Loukides G, Marchetti-Spaccamela A, Pissis S, Stougie L, et al.
Data Min Knowl Discov
. 2025 Jan;
39(2):12.
PMID: 39867462
Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence...
3.
Nguyen T, Le Nguyen T, Ifrim G
Data Min Knowl Discov
. 2024 Oct;
38(6):3372-3413.
PMID: 39473587
Time series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition, sports analytics and general sensing. In this...
4.
Javed A, Rizzo D, Lee B, Gramling R
Data Min Knowl Discov
. 2024 May;
38(3):813-839.
PMID: 38711534
Supplementary Information: The online version contains supplementary material available at 10.1007/s10618-023-00979-9.
5.
Meng J, Pitaksirianan N, Tu Y
Data Min Knowl Discov
. 2024 Feb;
34(4):980-1021.
PMID: 38390222
In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-graph as an effective model to represent information and its related graph mining techniques. In...
6.
Marques H, Swersky L, Sander J, Campello R, Zimek A
Data Min Knowl Discov
. 2023 Jul;
37(4):1473-1517.
PMID: 37424877
Supplementary Information: The online version contains supplementary material available at 10.1007/s10618-023-00931-x.
7.
Farhangi A, Bian J, Huang A, Xiong H, Wang J, Guo Z
Data Min Knowl Discov
. 2023 Apr;
37(3):1209-1229.
PMID: 37034121
Time series models often are impacted by extreme events and anomalies, both prevalent in real-world datasets. Such models require careful probabilistic forecasts, which is vital in risk management for extreme...
8.
Baniecki H, Parzych D, Biecek P
Data Min Knowl Discov
. 2023 Feb;
:1-37.
PMID: 36818741
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best?...
9.
Singh A, Blanco-Justicia A, Domingo-Ferrer J
Data Min Knowl Discov
. 2023 Jan;
:1-26.
PMID: 36619003
Reconciling machine learning with individual privacy is one of the main motivations behind federated learning (FL), a decentralized machine learning technique that aggregates partial models trained by clients on their...
10.
Zhang G, Gionis A
Data Min Knowl Discov
. 2023 Jan;
37(1):434-475.
PMID: 36618773
Decision trees are popular classification models, providing high accuracy and intuitive explanations. However, as the tree size grows the model interpretability deteriorates. Traditional tree-induction algorithms, such as C4.5 and CART,...