CIDM 2009:
Nashville,
TN,
USA
Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, part of the IEEE Symposium Series on Computational Intelligence 2009, Nashville, TN, USA, March 30, 2009 - April 2, 2009.
IEEE 2009 
Tutorial
- Fabian Mörchen:
Tutorial CIDM-T Temporal pattern mining in symbolic time point and time interval data.
CI/Probabilistic/Statistical and other methods I
- Barry Y. Chen, Tracy D. Lemmond, William G. Hanley:
Building ultra-low false alarm rate Support Vector Classifier ensembles using Random Subspaces.
1-8
- Su Chen, Tiejian Luo, Wei Liu, Yanxiang Xu:
Collaborative filtering with fine-grained trust metric.
9-16
- Willem S. van Heerden, Andries Petrus Engelbrecht:
HybridSOM: A generic rule extraction framework for self-organizing feature maps.
17-24
- Pei Li, Zhixu Li, Jun He, Xiaoyong Du, Hongyan Liu:
Assessing the influence probability between objects: A random walker approach.
25-32
CI/Probabilistic/Statistical and other methods II
- Yisong Chen, Hong Cui:
Intelligent feature extraction and knowledge mining by multivariate analyses.
33-39
- Yaohua Tang, Weimin Guo, Jinghuai Gao:
Efficient model selection for Support Vector Machine with Gaussian kernel function.
40-45
- Behrooz Safarinejadian, Mohammad B. Menhaj, Mehdi Karrari:
A fault tolerant peer-to-peer distributed EM algorithm.
46-52
- Oliver Schulte, Gustavo Frigo, Russell Greiner, Wei Luo, Hassan Khosravi:
A new hybrid method for Bayesian network learning With dependency constraints.
53-60
- Ali Ridho Barakbah, Yasushi Kiyoki:
A pillar algorithm for K-means optimization by distance maximization for initial centroid designation.
61-68
CI/Probabilistic/Statistical and other methods III
- Dengyuan Wu, Ying Liu, Ge Gao, Zhendong Mao, Weishan Ma, Tao He:
An adaptive ensemble classifier for concept drifting stream.
69-75
- Yang Zhang, Yuncai Liu:
Missing traffic flow data prediction using least squares support vector machines in urban arterial streets.
76-83
- Mehrnoush Famil Saeedian, Hamid Beigy:
Dynamic classifier selection using clustering for spam detection.
84-88
- Giuseppe Di Fatta, G. McC. Haworth, Kenneth W. Regan:
Skill rating by Bayesian inference.
89-94
- Pekka Siirtola, Perttu Laurinen, Eija Haapalainen, Juha Röning, Hannu Kinnunen:
Clustering-based activity classification with a wrist-worn accelerometer using basic features.
95-100
Data Understanding,
rule extraction,
logical models I
Applications to biomedicine,
e-commerce,
engineering,
etc I
Applications to biomedicine,
e-commerce,
engineering,
etc II
Applications to biomedicine,
e-commerce,
engineering,
etc III
Data Understanding,
rule extraction,
logical models II
Text,
graph and web mining
Mining spatial and spatial-temporal data
Mining of very large datasets,
scalability
Feature extraction,
selection,
aggregation,
construction
- Nhat Vo, Duc Vo, Subhash Challa, Bill Moran:
Parametric subspace analysis for dimensionality reduction and classification.
363-366
- Pablo Bermejo, José A. Gámez, Jose Miguel Puerta:
Incremental Wrapper-based subset Selection with replacement: An advantageous alternative to sequential forward selection.
367-374
- Stainam Nogueira Brandão, Wagner N. Silva, Luis A. E. Silva, Vladimir Fagundes, Carlos Eduardo R. de Mello, Geraldo Zimbrão, Jano Moreira de Souza:
Analysis and visualization of the geographical distribution of atlantic forest bromeliads species.
375-380
- Yongyan Wang, Kun Li, Hongan Wang:
Maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams.
381-388
Mining of signals and data stream
Last update Tue Feb 14 03:51:01 2012
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