Results 91 to 100 of about 303,598 (207)

Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models [PDF]

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix.
Sylvia Frühwirth-Schnatter, Christoph Pamminger   +1 more
core  

An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns

ISPRS International Journal of Geo-Information, 2016
Current time series clustering algorithms fail to effectively mine clustering distribution characteristics of time series data without sufficient prior knowledge.
Xiaomi Wang, Yaolin Liu, Yiyun Chen, Yi Liu   +3 more
doaj   +1 more source

R/BHC : fast Bayesian hierarchical clustering for microarray data [PDF]

, 2009
Background: Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained ...
Grant, Murray, Katherine J Denby, Savage, R, Savage, Richard S., Xu, Yang, Denby Katherine J, Denby, Katherine J., David L Wild, Ghahramani, Z, Grant Murray, Richard S Savage, Truman, W, Truman, William M., Grant, M, Wild, David L., Murray Grant, Denby, K, Savage Richard S, William M Truman, Xu, Y, Ghahramani Zoubin, Truman William M, Heller Katherine, Katherine Heller, ,, Wild David L, Heller, KA, Heller, K. (Katherine), Yang Xu, Wild, DL, Ghahramani, Zoubin, Zoubin Ghahramani, Xu Yang   +32 more
core   +1 more source

Time Series Clustering

, 2021
The notion of similarity is becoming more and more important in data analysis. Comparing two objects quantitatively is a fundamental building block of most machine learning algorithms, be it supervised or unsupervised. While for simple mathematical objects, the notion of similarity might be intuitively clear (when comparing how different two integers ...
openaire   +1 more source

Time Series Overlapping Clustering Based on Link Community Detection

IEEE Access
Given the nature of time series and their vast applications, it is essential to find clustering algorithms that depict their real-life properties.
Yasamin Ghahremani, Babak Amiri
doaj   +1 more source

Feature-based clustering of global sea level anomaly time series

Scientific Reports
The efficiency of various sea level change prediction methods can be enhanced through clustering global sea levels, considering the high dimensionality, redundancy, and nonlinearity of sea level anomaly time series.
Qinting Sun, Jianhua Wan, Shanwei Liu
doaj   +1 more source

Clustering financial time series [PDF]

, 2020
Time series clustering is heavily based on choosing a proper dissimilarity measure between a pair of time series. We present several dissimilarity measures and use two synthetic datasets to evaluate their performance. Hierarchical clustering and network
Potikyan, Nshan
core  

Clustering time series by extremal dependence

International Journal of Data Science and Analytics
Abstract The goal of this paper is to characterize the temporal dependence structure on the extremes of time series and use such dependency to group them. In particular, three similarity measures to capture extremal dependence are proposed, being their performance assessed in different scenarios.
Andrés M. Alonso, P. Gabirondo, Manuel G. Scotto   +2 more
openaire   +2 more sources

Clustering and Visualization of Multivariate Time Series

, 2010
The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS through unsupervised clustering and visualization.
Vellido Alcacena, Alfredo, Olier Caparroso, Iván   +1 more
openaire   +2 more sources

DYNAMIC TIME WARPING-BASED FUZZY C-MEANS WITH MULTIDIMENSIONAL SCALING FOR TIME SERIES CLUSTERING

Barekeng
Weather refers to atmospheric conditions such as temperature, humidity, air pressure, wind speed, and rainfall, all of which influence human activities.
Sri Hidayati, Regita Putri Permata, Fidi Wincoko Putro   +2 more
doaj   +1 more source