Advances in Knowledge Discovery and Data Mining

By Honghua (Kathy) Dai, Rayadurgam Srikant, Chengqi Zhang. 2004

2,311 citations recorded by the source corpus.

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

Citations in the corpus

Opening black box Data Mining models using Sensitivity Analysis (2011, methodology): “Such knowledge is important to check if the obtained model makes sense to the domain experts and if it is interesting (e.g. if it unveils potentially useful, interesting, or novel information) [1][3].”
Using sensitivity analysis and visualization techniques to open black box data mining models (2013, methodology): “In this paper, we propose a new visualization approach based on a Sensitivity Analysis (SA) to extract human understandable knowledge from supervised learning black box data mining models, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, including Random Forests (RF).”
Discovering Knowledge in Data: An Introduction to Data Mining (2005, methodology): “R for each numerical data mining methods include conceptual exercises which provide.”
A data mining approach to predict forest fires using meteorological data (2007, background): “On the other hand, the interest in Data Mining (DM), also known as Knowledge Discovery in Databases (KDD), arose due to the advances of Information Technology, leading to an exponential growth of business, scientific and engineering databases [8].”
Measuring transit use variability with smart-card data (2007, background): “Many algorithms for data mining may be found in Fayyad et al. (1996).”
Discrimination-aware classification (2011, methodology): “In this thesis, we focus on classification techniques only [40, 35, 84].”
Analysis of approximate nearest neighbor searching with clustered point sets (1999, neutral): “Nearest neighbor searching has applications in many areas, including knowledge discovery and data mining [FPSSU96], pattern recognition and classification [CH67, DH73], machine learning [CS93], data compression [GG92], multimedia databases [FSN95], document retrieval [DDF90], and statistics [DW82].”
A Descriptive Framework for the Field of Data Mining and Knowledge Discovery (2008, background): “fields, including databases, machines learning, pattern recognition, artificial intelligence, statistics, data visualization, mathematics and optimization [Fayyad et al. 1996a], and economics [Cheng et al. 2006].”
Techniques of Cluster Algorithms in Data Mining (2002, methodology): “Keywords: data mining, cluster algorithm, Condorcet’s criterion, demographic clustering”
Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling (2010, methodology): “The clustering methods are widely used in machine learning and data mining (Fayyad et al., 1996), data compression and vector quantization (Gersho and Gray, 1991), and pattern recognition and pattern classification (Duda and Hart, 1973).”
Automatic Subspace Clustering of High Dimensional Data (2005, background): “Data mining applications typically require cluster descriptions that can be easily assimilated by an end-user as insight and explanations are of critical importance (Fayyad et al., 1996).”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, background): “Evolution of most relevant Data Mining and Data Science models and methodologies (in white and light blue, respectively).”
An approach to discovering new technology opportunities: Keyword-based patent map approach (2009, background): “DM, also known as ‘knowledge discovery’, is a recent development for accessing and extracting information from databases (Fayyad et al., 1996).”