C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Machine Learning
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Mode-Finding for Mixtures of Gaussian Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data filtering for automatic classification of rocks from reflectance spectra
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Data mining tasks and methods: Classification: decision rules
Handbook of data mining and knowledge discovery
Data mining tasks and methods: Subgroup discovery: deviation analysis
Handbook of data mining and knowledge discovery
Intelligent data analysis
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Detection of emerging space-time clusters
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Estimation of regression contour clusters---an application of the excess mass approach to regression
Journal of Multivariate Analysis
Generalizing the Notion of Confidence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
The hunting of the bump: on maximizing statistical discrepancy
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Spatial scan statistics: approximations and performance study
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic classifier system and its application in data mining
Evolutionary Computation
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Confidence-based classifier design
Pattern Recognition
Detecting local regions of change in high-dimensional criminal or terrorist point processes
Computational Statistics & Data Analysis
Classification and filtering of spectra: A case study in mineralogy
Intelligent Data Analysis
Hybrid systems of local basis functions
Intelligent Data Analysis
Flexible patient rule induction method for optimizing process variables in discrete type
Expert Systems with Applications: An International Journal
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Supporting Factors in Descriptive Analysis of Brain Ischaemia
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Initializing Partition-Optimization Algorithms
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multivariate mode hunting: Data analytic tools with measures of significance
Journal of Multivariate Analysis
Evaluation Measures for Multi-class Subgroup Discovery
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Identification of interaction patterns and classification with applications to microarray data
Computational Statistics & Data Analysis
Responder identification in clinical trials with censored data
Computational Statistics & Data Analysis
Journal of Multivariate Analysis
A versatile model for packet loss visibility and its application to packet prioritization
IEEE Transactions on Image Processing
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Efficiently mining regional outliers in spatial data
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
PRIM versus CART in subgroup discovery: When patience is harmful
Journal of Biomedical Informatics
First-Order Multi-class Subgroup Discovery
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Expert Systems with Applications: An International Journal
SD-map: a fast algorithm for exhaustive subgroup discovery
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Market basket analysis of retail data: supervised learning approach
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Different slopes for different folks: mining for exceptional regression models with cook's distance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Subgroup discovery using bump hunting on multi-relational histograms
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Many objective robust decision making for complex environmental systems undergoing change
Environmental Modelling & Software
Improving scenario discovery using orthogonal rotations
Environmental Modelling & Software
On detection of emerging anomalous traffic patterns using GPS data
Data & Knowledge Engineering
Design configuration selection for hard-error reliable processors via statistical rules
Microprocessors & Microsystems
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Many data analytic questions can be formulated as (noisy) optimization problems. They explicitly or implicitly involve finding simultaneous combinations of values for a set of (’’input‘‘) variables that imply unusually large (or small) values of another designated (’’output‘‘) variable. Specifically, one seeks a set of subregions of the input variable space within which the value of the output variable is considerably larger (or smaller) than its average value over the entire input domain. In addition it is usually desired that these regions be describable in an interpretable form involving simple statements (’’rules‘‘) concerning the input values. This paper presents a procedure directed towards this goal based on the notion of ’’patient‘‘ rule induction. This patient strategy is contrasted with the greedy ones used by most rule induction methods, and semi-greedy ones used by some partitioning tree techniques such as CART. Applications involving scientific and commercial data bases are presented.