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» Evaluating algorithms that learn from data streams
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DIS
2006
Springer
14 years 7 days ago
Kalman Filters and Adaptive Windows for Learning in Data Streams
We study the combination of Kalman filter and a recently proposed algorithm for dynamically maintaining a sliding window, for learning from streams of examples. We integrate this i...
Albert Bifet, Ricard Gavaldà
BIBE
2009
IEEE
131views Bioinformatics» more  BIBE 2009»
13 years 12 months ago
Learning Scaling Coefficient in Possibilistic Latent Variable Algorithm from Complex Diagnosis Data
—The Possibilistic Latent Variable (PLV) clustering algorithm is a powerful tool for the analysis of complex datasets due to its robustness toward data distributions of different...
Zong-Xian Yin
DIS
2009
Springer
14 years 3 months ago
A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams
Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered...
Fabio Fumarola, Anna Ciampi, Annalisa Appice, Dona...
PODS
2004
ACM
137views Database» more  PODS 2004»
14 years 8 months ago
On the Memory Requirements of XPath Evaluation over XML Streams
The important challenge of evaluating XPath queries over XML streams has sparked much interest in the past few years. A number of algorithms have been proposed, supporting wider f...
Ziv Bar-Yossef, Marcus Fontoura, Vanja Josifovski
DEXA
2009
Springer
151views Database» more  DEXA 2009»
14 years 3 months ago
Detecting Projected Outliers in High-Dimensional Data Streams
Abstract. In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector ...
Ji Zhang, Qigang Gao, Hai H. Wang, Qing Liu, Kai X...