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OR
2006
Springer
9 years 9 months ago
Financial forecasting through unsupervised clustering and neural networks
In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and...
Nicos G. Pavlidis, Vassilis P. Plagianakos, Dimitr...
ICASSP
2010
IEEE
9 years 9 months ago
Learning from high-dimensional noisy data via projections onto multi-dimensional ellipsoids
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
Liuling Gong, Dan Schonfeld
MLMTA
2007
9 years 11 months ago
A Novel Hybrid Neural Network for Data Clustering
- Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer either poor performance of unsupervised lea...
Donghai Guan, Andrey Gavrilov, Weiwei Yuan, Young-...
SBCCI
2003
ACM
129views VLSI» more  SBCCI 2003»
10 years 2 months ago
Hyperspectral Images Clustering on Reconfigurable Hardware Using the K-Means Algorithm
Unsupervised clustering is a powerful technique for understanding multispectral and hyperspectral images, being k-means one of the most used iterative approaches. It is a simple th...
Abel Guilhermino S. Filho, Alejandro César ...
GECCO
2004
Springer
121views Optimization» more  GECCO 2004»
10 years 2 months ago
Network Intrusion Detection Using Genetic Clustering
Abstract. We apply the Unsupervised Niche Clustering (UNC), a genetic niching technique for robust and unsupervised clustering, to the intrusion detection problem. Using the normal...
Elizabeth Leon, Olfa Nasraoui, Jonatan Góme...
SAC
2004
ACM
10 years 3 months ago
Unsupervised learning techniques for an intrusion detection system
With the continuous evolution of the types of attacks against computer networks, traditional intrusion detection systems, based on pattern matching and static signatures, are incr...
Stefano Zanero, Sergio M. Savaresi
SYNASC
2005
IEEE
170views Algorithms» more  SYNASC 2005»
10 years 3 months ago
Density Based Clustering with Crowding Differential Evolution
The aim of this work is to analyze the applicability of crowding differential evolution to unsupervised clustering. The basic idea of this approach, interpreting the clustering pr...
Daniela Zaharie
AVSS
2007
IEEE
10 years 3 months ago
Vehicular traffic density estimation via statistical methods with automated state learning
This paper proposes a novel approach of combining an unsupervised clustering scheme called AutoClass with Hidden Markov Models (HMMs) to determine the traffic density state in a R...
Evan Tan, Jing Chen
ICPR
2008
IEEE
10 years 4 months ago
Learning visual dictionaries and decision lists for object recognition
Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictiona...
Wei Zhang, Thomas G. Dietterich
ICPR
2008
IEEE
10 years 4 months ago
Unsupervised clustering using hyperclique pattern constraints
A novel unsupervised clustering algorithm called Hyperclique Pattern-KMEANS (HP-KMEANS) is presented. Considering recent success in semisupervised clustering using pair-wise const...
Yuchou Chang, Dah-Jye Lee, James K. Archibald, Yi ...
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