10 Challenging Problems in Data Mining Research

By Qiang Yang, Xindong Wu. International Journal of Information Technology and Decision Making, 2006

966 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e3a617f1848f8df98d28472e32d9c2ec3dcb8ad3

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In Information Systems

Citations in the corpus

A review of unsupervised feature learning and deep learning for time-series modeling (2014, background): “Analysis of time-series data has been the subject of active research for decades [66,26] and is considered by Yang and Wu [131] as one of the top 10 challenging problems in data mining due to its unique properties.”
Top 10 algorithms in data mining (2007, background): “Wu was giving a seminar on 10 Challenging Problems in Data Mining Research [89] at PolyU.”
Class Imbalance, Redux (2011, background): “Indeed, the problem of inducing classifiers over imbalanced datasets with asymmetric costs was recently designated as one of '10 challenging problems in data mining research' [21].”
Cost-sensitive learning methods for imbalanced data (2010, background): “It is also considered as one of ten challenging problems in data mining research [3].”
Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems (2009, background): “In the last years, the class imbalance problem is one of the emergent challenges in data mining (DM) [45].”
A survey of data mining and knowledge discovery process models and methodologies (2010, background): “In the study by Yang and Wu (2006), one of the 10 challenging problems to be solved in data mining research is the need to build a new methodology to help users avoid many common data mining mistakes, by improving the automation of the KDD process.”
Bandit-Based Automated Machine Learning (2018, methodology): “As preprocessing, we choose Feature Selection (FS) and Feature Extraction (FE) methods, because data with extremely high dimensionality has presented one of the most prominent challenges to existing learning methods [13], and these are the most widely employed techniques for reducing dimensionality ”
A novel ensemble method for classifying imbalanced data (2015, background): “The imbalance problem is one of the top 10 challenging problems in data mining [42].”
Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach (2016, methodology): “Adapting existing methods for use with streams is challenging, it remains an active goal of data mining research (Yang & Wu 2006; Gaber, Zaslavsky & Krishnaswamy 2007).”
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding (2021, background): “Real-world time-series data is high dimensional, complex, and has unique properties that bring about many challenges for data modeling (Yang & Wu, 2006).”
A Survey on Evolutionary Instance Selection and Generation (2010, background): “In recent years, the data mining community has identified some challenging problems in the area (Yang & Wu, 2006).”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, background): “This situation is known as the class imbalance problem [29,66,118] and it is common in many real problems from telecommunications, web, finance-world, ecology, biology, medicine not only, and which can be considered one of the top problems in data mining today [143].”
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning (2016, background): “The class imbalance problem has been encountered in multiple areas such as telecommunication managements, bioinformatics, fraud detection, and medical diagnosis, and has been considered one of the top 10 problems in data mining and pattern recognition (Yang and Wu, 2006; Rastgoo et al., 2016).”
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012, background): “The problem of imbalanced datasets is extremely significant (Yang & Wu, 2006) because it is implicit in most real world applications, such as very high resolution airborne imagery (Chen, Fang, Huo, & Li, 2011), e-mail foldering (Bermejo, Gámez, & Puerta, 2011) or micro seismic hazards in coal mines…”
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013, background): “This problem occurs in many real-world classification tasks and has been defined as a challenge for the Data Mining community [65].”