Data Mining and Knowledge Discovery

By Chao Zhang, Jiawei Han. 2005

333 citations recorded by the source corpus.

The premier technical publication in the field, Data Mining and Knowledge Discovery is a resource collecting relevant common methods and techniques and a forum for unifying the diverse constituent research communities. The journal publishes original technical papers in both the research and practice of data mining and knowledge discovery, surveys and tutorials of important areas and techniques, and detailed descriptions of significant applications. Coverage includes: Theory and Foundational Issues Data Mining Methods Algorithms for Data Mining Knowledge Discovery Process Application Issues.

Subfields: Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Weichen Shen
2. Fei Sun
3. Keping Yang
4. Eirini Ntoutsi
5. Wenbin Zhang
6. Yu Zhu
7. Yufei Feng
8. Menghan Wang

Citations in the corpus

• FAHT: An Adaptive Fairness-aware Decision Tree Classifier (2019, background): “[Calders and Verwer, 2010] proposes three approaches for fairness-aware Naı̈ve Bayes classifiers: the first one alters the decision distribution until there is no more discrimination, the second one attempts to remove the correlation between sensitive attribute and class label by building a separate”
• Deep Session Interest Network for Click-Through Rate Prediction (2019, methodology): “General Factorization Framework (GFF) [ Hidasi and Tikk, 2016 ] uses sum pooling of items to represent a session.”
• HIVE-COTE 2.0: a new meta ensemble for time series classification (2021, methodology): “TDE transforms the window using the Symbolic Fourier Approximation (SFA) (Schäfer and Högqvist, 2012) transform proposed for BOSS (Schäfer, 2015).”