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AAAI
2011
12 years 5 months ago
Temporal Dynamics of User Interests in Tagging Systems
Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation systems generally model user behavior as r...
Dawei Yin, Liangjie Hong, Zhenzhen Xue, Brian D. D...
CIKM
2010
Springer
13 years 4 months ago
Partial drift detection using a rule induction framework
The major challenge in mining data streams is the issue of concept drift, the tendency of the underlying data generation process to change over time. In this paper, we propose a g...
Damon Sotoudeh, Aijun An
IDA
2002
Springer
13 years 5 months ago
Online classification of nonstationary data streams
Most classification methods are based on the assumption that the data conforms to a stationary distribution. However, the real-world data is usually collected over certain periods...
Mark Last
TKDE
2008
158views more  TKDE 2008»
13 years 5 months ago
Hierarchical Clustering of Time-Series Data Streams
This paper presents a time series whole clustering system that incrementally constructs a tree-like hierarchy of clusters, using a top-down strategy. The Online Divisive-Agglomera...
Pedro Pereira Rodrigues, João Gama, Jo&atil...
INFORMATICALT
2008
196views more  INFORMATICALT 2008»
13 years 5 months ago
An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams
Abstract. Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data blo...
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
ASC
2008
13 years 5 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
COMAD
2009
13 years 6 months ago
Categorizing Concepts for Detecting Drifts in Stream
Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Se...
Sharanjit Kaur, Vasudha Bhatnagar, Sameep Mehta, S...
DEXA
2008
Springer
123views Database» more  DEXA 2008»
13 years 7 months ago
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
Abstract. We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effectiv...
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
CBMS
2008
IEEE
13 years 7 months ago
Effectiveness of Local Feature Selection in Ensemble Learning for Prediction of Antimicrobial Resistance
In the real world concepts are often not stable but change over time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may ...
Seppo Puuronen, Mykola Pechenizkiy, Alexey Tsymbal
KDD
1998
ACM
181views Data Mining» more  KDD 1998»
13 years 9 months ago
Approaches to Online Learning and Concept Drift for User Identification in Computer Security
The task in the computer security domain of anomaly detection is to characterize the behaviors of a computer user (the `valid', or `normal' user) so that unusual occurre...
Terran Lane, Carla E. Brodley