• Publications
  • Influence
Data clustering: a review
TLDR
An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Data Clustering: 50 Years Beyond K-means
The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding
Statistical Pattern Recognition: A Review
TLDR
The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Handbook of Fingerprint Recognition
TLDR
This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
An introduction to biometric recognition
TLDR
A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
TLDR
A fast fingerprint enhancement algorithm is presented, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency.
Unsupervised Learning of Finite Mixture Models
TLDR
The novelty of the approach is that it does not use a model selection criterion to choose one among a set of preestimated candidate models; instead, it seamlessly integrate estimation and model selection in a single algorithm.
Combining multiple clusterings using evidence accumulation
  • A. Fred, Anil K. Jain
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 June 2005
TLDR
A theoretical framework for the analysis of the proposed clustering combination strategy and its evaluation is developed, based on the concept of mutual information between data partitions, for extracting a consistent clustering, given the various partitions in a clustering ensemble.
A modified Hausdorff distance for object matching
TLDR
Based on experiments on synthetic images containing various levels of noise, the authors determined that one of these distance measures, called the modified Hausdorff distance (MHD) has the best performance for object matching.
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