Search Results - Clustering of Time Series Data and Algorithms

Bayesian Biclustering by dynamics: A clustering algorithm for SAGD time series data by Pinto, Helen, Gates, Ian, Wang, Xin

“… This is a greedy algorithm that automatically clusters both rows and columns of SAGD injection and production data and then generates a descriptive summary for each cluster…”
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A modified Kohonen map algorithm for clustering time series data by Jayanth Krishnan, Kalpathy, Mitra, Kishalay

“… Though a popular clustering algorithm such as K-Means is capable of performing vector quantization, the averaging technique to compute centroids in the algorithm is not well suited to handle time series data…”
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Automatic classification of spread‐F types in ionogram images using support vector machine and convolutional neural network by Phongsachot Benchawattananon, Apirat Siritaratiwat, Pornchai Supnithi, Michi Nishioka, Septi Perwitasari, Puripong Suthisopapan, A. Kruesubthaworn

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A Topological-Indicators-Based k-Means Clustering Algorithm and Its Application in Time Series Data: A Case Study on Sea Level Variability in Peninsular Malaysia by Lin, Zixin, Zulkepli, Nur Fariha Syaqina, Bin Mohd Kasihmuddin, Mohd Shareduwan, Gobithaasan, Rudrusamyr

“…Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels…”
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Discovery of Loose Group Companion From Trajectory Data Streams by Thi Thi Shein, Sutheera Puntheeranurak, Makoto Imamura

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Clustering short time series gene expression data by Ernst, Jason, Nau, Gerard J., Bar-Joseph, Ziv

“… Most clustering algorithms are unable to distinguish between real and random patterns. Results: We present an algorithm specifically designed for clustering short time series expression data…”
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Model-based clustering of regression time series data via APECM-an AECM algorithm sung to an even faster beat by Chen, Wei-Chen, Maitra, Ranjan

“…We propose a model‐based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian…”
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A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique by Jalali, Alireza, Shayegan, Mohammad Amin, Jalab, Hamid A., Herawan, Tutut, Teh, Ying-Wah, Aghabozorgi, Said

“… However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data…”
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Multivariate time series clustering analysis of the Global Dietary Database to uncover patterns in dietary trends (1990–2018) by Matousek, Adriano, Leung, Tiffany H, Pang, Herbert

“…). This study aims to extend an existing multivariate time series (MTS) clustering algorithm to allow for greater customisability and provide the first cluster analysis…”
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A simplified swarm optimization-based restricted Boltzmann machine algorithm for a time series clustering by Zulvia, Ferani E., Kuo, R.J.

“… However, most existing clustering algorithms are not specialized for time series data. Therefore, this study introduces a metaheuristics-based approach, utilizing a restricted Boltzmann machine (RBM…”
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A Distributed Information Granulation Method for Time Series Clustering by Mu, Yashuang, Liu, Tian, Zhang, Wenqiang, Guo, Hongyue, Wang, Lidong, Liu, Xiaodong

“… With the rapid increase in the amount of time series data, many traditional clustering algorithms cannot directly deal with large…”
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A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT by Li, Haibo, Tong, Juncheng

“…In smart environments based on the Internet of Things (IoT), almost all of the object information that is collected by various sensors is time series data, which records the behavior of the objects…”
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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance by Liu, Yongli, Chen, Jingli, Wu, Shuai, Liu, Zhizhong, Chao, Hao

“… Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance…”
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Unraveling Meteorological Dynamics: A Two-Level Clustering Algorithm for Time Series Pattern Recognition with Missing Data Handling by Skamnia, Ekaterini, Bekri, Eleni S., Economou, Polychronis

“… In this work, a time series clustering procedure composed of two levels is proposed, focusing on clustering spatial units…”
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Time-series clustering – A decade review by Aghabozorgi, Saeed, Seyed Shirkhorshidi, Ali, Ying Wah, Teh

“… to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets…”
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An approach based on data mining and genetic algorithm to optimizing time series clustering for efficient segmentation of customer behavior by Hamidi, Hodjat (Hojatollah), Haghi, Bahare

“… This research addresses this critical gap by introducing an innovative time series-based approach for customer behavior segmentation…”
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A clustering algorithm for detecting differential deviations in the multivariate time-series IoT data based on sensor relationship by Idrees, Rabbia, Maiti, Ananda, Garg, Saurabh

“…Internet-of-things (IoT) applications involve a large number of sensors reporting data as a set of time series…”
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Interpolation based consensus clustering for gene expression time series by Chiu, Tai-Yu, Hsu, Ting-Chieh, Yen, Chia-Cheng, Wang, Jia-Shung

“…Background Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarray…”
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Hierarchical clustering of time series data with parametric derivative dynamic time warping by Luczak, Maciej

“…) is a popular and efficient distance measure used in classification and clustering algorithms applied to time series data…”
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Cluster-based stability evaluation in time series data sets by Klassen, Gerhard, Tatusch, Martha, Conrad, Stefan

“… This is especially the case for clustering algorithms. Although there are a few evolutionary approaches for time-dependent data, the evaluation of these and therefore the selection is difficult for the user…”
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