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» Online Data Mining for Co-Evolving Time Sequences
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KDD
1998
ACM
141views Data Mining» more  KDD 1998»
15 years 2 months ago
Rule Discovery from Time Series
We consider the problem of nding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple examp...
Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Ren...
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
15 years 4 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun
91
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ICDM
2003
IEEE
240views Data Mining» more  ICDM 2003»
15 years 3 months ago
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences, extracted via a sliding window, has received much attention...
Eamonn J. Keogh, Jessica Lin, Wagner Truppel
SADM
2010
128views more  SADM 2010»
14 years 8 months ago
Online training on a budget of support vector machines using twin prototypes
: This paper proposes twin prototype support vector machine (TVM), a constant space and sublinear time support vector machine (SVM) algorithm for online learning. TVM achieves its ...
Zhuang Wang, Slobodan Vucetic
KDD
2004
ACM
144views Data Mining» more  KDD 2004»
15 years 10 months ago
IncSpan: incremental mining of sequential patterns in large database
Many real life sequence databases, such as customer shopping sequences, medical treatment sequences, etc., grow incrementally. It is undesirable to mine sequential patterns from s...
Hong Cheng, Xifeng Yan, Jiawei Han