BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
AANN: an alternative to GMM for pattern recognition
Neural Networks
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Password Memorability and Security: Empirical Results
IEEE Security and Privacy
Typing Patterns: A Key to User Identification
IEEE Security and Privacy
Outlier Mining in Large High-Dimensional Data Sets
IEEE Transactions on Knowledge and Data Engineering
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Estimating the Support of a High-Dimensional Distribution
Neural Computation
From outliers to prototypes: Ordering data
Neurocomputing
Supporting diagnosis of attention-deficit hyperactive disorder with novelty detection
Artificial Intelligence in Medicine
Locally linear reconstruction for instance-based learning
Pattern Recognition
Minimum spanning tree based one-class classifier
Neurocomputing
Constructing response model using ensemble based on feature subset selection
Expert Systems with Applications: An International Journal
User authentication through typing biometrics features
IEEE Transactions on Signal Processing
EvIdentTM: a functional magnetic resonance image analysis system
Artificial Intelligence in Medicine
Condition monitoring of 3G cellular networks through competitive neural models
IEEE Transactions on Neural Networks
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The purpose of novelty detection is to detect (novel) patterns that are not generated by the identical distribution of the normal class. A distance-based novelty detector classifies a new data pattern as ''novel'' if its distance from ''normal'' patterns is large. It is intuitive, easy to implement, and fits naturally with incremental learning. Its performance is limited, however, because it relies only on distance. In this paper, we propose considering topological relations as well. We compare our proposed method with 13 other novelty detectors based on 21 benchmark data sets from two sources. We then apply our method to a real-world application in which incremental learning is necessary: keystroke dynamics-based user authentication. The experimental results are promising. Not only does our method improve the performance of distance-based novelty detectors, but it also outperforms the other non-distance-based algorithms. Our method also allows efficient model updates.