David J. Hand mostly deals with Data mining, Statistics, Artificial intelligence, Data science and Econometrics. When carried out as part of a general Data mining research project, his work on Data stream mining, Knowledge extraction and Data pre-processing is frequently linked to work in Concept mining, therefore connecting diverse disciplines of study. His work carried out in the field of Data pre-processing brings together such families of science as Association rule learning, Metadata, Missing data and Statistical model.
His Multivariate analysis of variance study, which is part of a larger body of work in Statistics, is frequently linked to Scientific publishing, Statistical software, Quarter century and Pace, bridging the gap between disciplines. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Pattern recognition. He interconnects Goodness of fit, Bayes' theorem, Linear model and Idiot in the investigation of issues within Econometrics.
David J. Hand mainly focuses on Artificial intelligence, Data mining, Statistics, Machine learning and Data science. Artificial intelligence is frequently linked to Pattern recognition in his study. David J. Hand performs multidisciplinary study on Data mining and Context in his works.
Many of his studies on Statistics apply to Econometrics as well.
David J. Hand focuses on Data science, Artificial intelligence, Statistics, Data mining and Econometrics. In his study, which falls under the umbrella issue of Data science, Statistics education is strongly linked to Big data. His Artificial intelligence research includes themes of Measure, Machine learning and Pattern recognition.
His Machine learning research is multidisciplinary, relying on both Classification methods, Algorithm, Adaptive filter and Forgetting. The study incorporates disciplines such as Inference and Sustainable development in addition to Statistics. His specific area of interest is Data mining, where he studies Anomaly detection.
The scientist’s investigation covers issues in Artificial intelligence, Data science, Classifier, Pattern recognition and Statistics. His Artificial intelligence research focuses on Machine learning and how it relates to Algorithm, Forgetting and Adaptive filter. His Forgetting research focuses on subjects like Range, which are linked to Data mining.
His Data mining study combines topics in areas such as Linear subspace, Bayesian probability and Missing data. His research integrates issues of Confidentiality, Human condition, Data analysis and Big data in his study of Data science. His study in Pattern recognition is interdisciplinary in nature, drawing from both Measure, Distribution and Area under the roc curve.
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Principles of Data Mining
David J. Hand;Padhraic Smyth;Heikki Mannila.
Top 10 algorithms in data mining
Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh.
Knowledge and Information Systems (2007)
Principles of Data Mining
David J. Hand;Heikki Mannila;Padhraic Smyth.
Research Papers in Economics (2001)
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
David J. Hand;Robert J. Till.
Machine Learning (2001)
Finite Mixture Distributions
Brian Everitt;D. J. Hand.
Discrimination and Classification
David J. Hand.
Statistical Fraud Detection: A Review
Richard J Bolton;David J. Hand.
Statistical Science (2002)
Analysis of Repeated Measures
Martin J. Crowder;David J. Hand.
Statistical Classification Methods in Consumer Credit Scoring: a Review
D. J. Hand;W. E. Henley.
Journal of The Royal Statistical Society Series A-statistics in Society (1997)
Construction and Assessment of Classification Rules
David J. Hand.
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