Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A resource-allocating network for function interpolation
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining and knowledge discovery in databases
Communications of the ACM
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Data Compression and Local Metrics for Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Efficient Locally Weighted Polynomial Regression Predictions
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiresolution instance-based learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Scale-based clustering using the radial basis function network
IEEE Transactions on Neural Networks
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Visualization and Aggregation of Nearest Neighbor Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Gesture Recognition Driven by the Spatial Context of Strokes
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Combining Feature Reduction and Case Selection in Building CBR Classifiers
IEEE Transactions on Knowledge and Data Engineering
Enhancing Density-Based Data Reduction Using Entropy
Neural Computation
A New Density-Based Scheme for Clustering Based on Genetic Algorithm
Fundamenta Informaticae
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
A Density-Based Data Reduction Algorithm for Robust Estimators
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
A scalable framework for cluster ensembles
Pattern Recognition
Fast approximate spectral clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Concept sampling: towards systematic selection in large-scale mixed concepts in machine learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Computation of initial modes for K-modes clustering algorithm using evidence accumulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IEEE Transactions on Image Processing
Neural Network Ensembles from Training Set Expansions
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Key Point Based Data Analysis Technique
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Fast Parzen Window density estimator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
K-means clustering seeds initialization based on centrality, sparsity, and isotropy
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
DisClus: a distributed clustering technique over high resolution satellite data
ICDCN'10 Proceedings of the 11th international conference on Distributed computing and networking
SATCLUS: an effective clustering technique for remotely sensed images
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Isolating top-k dense regions with filtration of sparse background
Pattern Recognition Letters
Case-based classifiers with fuzzy rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Density-based image vector quantization using a genetic algorithm
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
An affinity-based new local distance function and similarity measure for kNN algorithm
Pattern Recognition Letters
A grid-density based technique for finding clusters in satellite image
Pattern Recognition Letters
Local pattern detection and clustering
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Dynamic data condensation for classification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A New Density-Based Scheme for Clustering Based on Genetic Algorithm
Fundamenta Informaticae
Multi-level Low-rank Approximation-based Spectral Clustering for image segmentation
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
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A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.