Results 111 to 120 of about 303,598 (207)

On time series clustering with k-means

CoRR
There is a long history of research into time series clustering using distance-based partitional clustering. Many of the most popular algorithms adapt k-means (also known as Lloyd's algorithm) to exploit time dependencies in the data by specifying a time series distance function.
Christopher Holder, Anthony J. Bagnall, Jason Lines   +2 more
openaire   +2 more sources

TSCAPE: time series clustering with curve analysis and projection on an Euclidean space

Connection Science
The ever-growing use of digital systems has led to the accumulation of vast datasets, particularly time series, depicting the temporal evolution of variables and systems.
Jeremy Renaud, Raphaël Couturier, Christophe Guyeux, Benoit Courjal   +3 more
doaj   +1 more source

Storm surge time series de-clustering using correlation analysis

Weather and Climate Extremes
The extraction of individual events from continuous time series is a common challenge in many extreme value studies. In the field of environmental science, various methods and algorithms for event identification (de-clustering) have been applied in the ...
Ariadna Martín, Thomas Wahl, Alejandra R. Enriquez, Robert Jane   +3 more
doaj   +1 more source

Clustering distributed time series in sensor networks [PDF]

, 2008
Mohamed Medhat Gaber, Gaber, M., Yin, J., Jie Yin   +3 more
core   +1 more source

Functional model-based curve clustering for discovering temporal patterns in chronological corpora

, 2012
In many applications of textual analysis corpora are characterized by a temporal structure, i.e. they include texts which have a chronological order (e.g.: political discourses, institutional documents, articles published in newspapers, messages posted ...
Trevisani, Matilde, TUZZI, ARJUNA, A. Tuzzi   +2 more
core  

Unfolding preprocessing for meaningful time series clustering.

, 2006
Clustering methods are commonly applied to time series, either as a preprocessing stage for other methods or in their own right. In this paper it is explained why time series clustering may sometimes be considered as meaningless.
Simon, Geoffroy, Verleysen, Michel, Lee, John Aldo   +2 more
core   +1 more source

DDG-clustering: a novel technique for highly accurate results [PDF]

, 2009
A key to the success of any clustering algorithm is the similarity measure applied. The similarity among different instances is defined according to a particular criterion.
Gaber, Mohamed Medhat, Ammar, Z., Gaber, M., Abdallah, Zahraa S.   +3 more
core  

Clustering-based Behavioural Analysis of Biological Objects

The article examines the problem of processing short time series for bioinformatics tasks using data mining methods in the field of pharmacology. The experiments were conducted using heart contraction (contraction and relaxation) power data that were ...
Kiršners, Arnis
core  

Parsimonious clustering of time series

, 2015
Time series arise in many areas, including engineering, computer science, medical science, social science and economics. Clustering of time series has become an important topic, motivated by the increased interest in these type of data.
D'AMBROSIO, ANTONIO, IORIO, CARMELA, SICILIANO, ROBERTA, Frasso, Gianluca   +3 more
core  

Time Series Regression With Meta-Clusters

, 2013
This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain subgroups of time series data with normal distribution from the inflow into wastewater treatment plant data, composed of several groups ...
openaire   +3 more sources