From Data Mining to Knowledge Discovery in Databases

By Usama M. Fayyad, G. Piatetsky-Shapiro, Padhraic Smyth. 1996

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Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “(Examples of such pipelines include KDD, CRISP-DM, or the CCC Big Data Pipelines; see Figure 1, or Fayyad et al. 1996; Chapman et al. 2000; Agrawal et al. 2012.)”
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “tative of the analysis one would perform on a real problem. It is also not clear why the comparison should be performed on a static dataset, because any formal process for deﬁning knowledge from data [15, 16, 17] would require an iterative process, where one reﬁnes the data processing after interpre”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, methodology): “…are constructed as part of the data science process, there tends to be little difference between algorithms, assuming that the data scientist follows a standard process for knowledge discovery [such as KDD, CRISP-DM, or BigData, see Fayyad et al., 1996, Chapman et al., 2000, Agrawal et al., 2012].”
Problem Formulation and Fairness (2019, background): “Fayyad, Piatetsky-Shapiro, and Smyth [14] make a point of saying that “data mining is a legitimate activity as long as one understands how to do it,” suggesting that there is a particular way to go about mining data to ensure appropriate results.”
Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms (2014, unknown): “d data mining process (Fayyad et al., 1996).”
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, unknown): “It is part of a more general process of knowledge discovery in databases (KDD), which also includes steps such as data preparation, selection, and cleaning [65].”
Extracting comprehensible models from trained neural networks (1996, background): “For example, in order to apply a learned model to a large body of data, one might want to translate a learned hypothesis (or part of one) into a standard query language so that it can be used to query a database (Fayyad et al., 1996; Pazzani et al., 1996).”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, methodology): “Thus, data mining is considered as part of the KDD, and data mining in the KDD process is a set of applications of specific algorithms for extracting patterns from preprocessed or “ready-to-data-mine” data (as opposed to raw data in operational databases) [6, 9 ].”
Bandit-Based Automated Machine Learning (2018, methodology): “The process of Knowledge Discovery in Databases involves several steps, such as selecting a subset of data, cleaning and transforming data, extracting features, choosing appropriate data mining techniques for pattern extraction, evaluating and interpreting results, and implementing such results [1].”
From Local Patterns to Global Models: The LeGo Approach to Data Mining (2008, methodology): “The typical process model, as it has been sketched in similar ways in numerous publications, going back to [11].”
Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research (2016, background): “In 1996, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth published a number of articles [12], [13, 14] which build the base for what we call now the process of knowledge discovery in databases.”
Genetic Process Mining (2005, background): “Actually, it assumes that his mining technique is embedded in a Knowledge Discovery Database (KDD) process [39].”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “If one only cleans two of the data points, the intermediate result reveals a misleading trend (Figure 1b).”
On the predictive power of meta-features in OpenML (2017, background): “The process itself, also known as knowledge discovery, consists of several steps, such as data selection, data pre-processing, data mining, and evaluation or interpretation (Fayyad et al., 1996); see Fig.”