Results 61 to 70 of about 303,598 (207)
Clustering of streaming time series is meaningless [PDF]
Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery - DMKD '03, 2003 Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it's own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization,
Jessica Lin 0001 +2 more
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A Fast Weighted Fuzzy C-Medoids Clustering for Time Series Data Based on P-Splines
Sensors, 2022 The rapid growth of digital information has produced massive amounts of time series data on rich features and most time series data are noisy and contain some outlier samples, which leads to a decline in the clustering effect. To efficiently discover the
Jiucheng Xu +4 more
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Special Issue on ‘Time series clustering’ [PDF]
Advances in Data Analysis and Classification, 2011 There exists a great variety of clustering methods for multidimensional data points or dissimilarity data, they are standard tools in many statistical packages or data mining systems. More complex data types require special approaches, either by a reduction to classical cases by suitable preprocessing steps, by specifying appropriate dissimilarity ...
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Graph-based two-level clustering for electric vehicle usage patterns
Energy and AIElectric vehicles (EVs) continue to gain popularity over internal combustion vehicles, but driving and charging an EV is a fundamentally different experience. EV usage patterns include features such as charging speed, battery state of charge, and battery
Dhanashree Balaram +5 more
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Feature-Driven Time Series Clustering
, 2021 International audienceThe problem of clustering time series has several applications in real-life contexts, especially in data science and data analytics pipelines.
Ng, Raymond +2 more
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In-Database Time Series Clustering
Proceedings of the ACM on Management of DataTime series data are often clustered repeatedly across various time ranges to mine frequent subsequence patterns from different periods, which could further support downstream applications. Existing state-of-the-art (SOTA) time series clustering method, such as K-Shape, can proficiently cluster time series data referring to their shapes.
Yunxiang Su, Kenny Ye Liang, Shaoxu Song
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Partial mixture model for tight clustering of gene expression time-course [PDF]
, 2008 Background: Tight clustering arose recently from a desire to obtain tighter and potentially more informative clusters in gene expression studies. Scattered genes with relatively loose correlations should be excluded from the clusters.
Li Chang-Tsun +8 more
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Time Series Clustering Based on Singularity [PDF]
, 2017 With relevant theories on time series clustering, the thesis makes research into similarity clustering process of time series from the perspective of singularity and proposes the time series clustering based on singularity applying K-means and DBScan ...
Ding, Xueli, Chang, Dan, Ma, Yunfang
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Multivariate Time Series Density Clustering Algorithm Using Shapelet Space [PDF]
Jisuanji kexue yu tansuoMultivariate time series clustering has become an important research topic in the task of time series analysis. Compared with univariate time series, the research of multivariate time series is more complex and difficult.
SHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui
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Guided conjugate Bayesian clustering for uncovering rhythmically expressed genes [PDF]
, 2006 Background: An increasing number of microarray experiments produce time series of expression levels for many genes. Some recent clustering algorithms respect the time ordering of the data and are, importantly, extremely fast. The focus of this paper is
Millar, A. J. +3 more
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