Simulation Study on the Electricity Data Streams Time Series Clustering
نویسندگان
چکیده
منابع مشابه
Hierarchical Time-Series Clustering for Data Streams⋆
This paper presents a time-series whole clustering system that incrementally constructs a hierarchy of clusters. The Online DivisiveAgglomerative Clustering (ODAC) system is an incremental implementation of divisive analysis clustering, using the correlation between timeseries as similarity measure. The system tests existing clusters by descending order of diameters, looking for a possible bina...
متن کاملClustering of Time-Series Data Streams
This paper presents a time-series whole clustering system that incrementally constructs a tree-like hierarchy of clusters. The Online DivisiveAgglomerative Clustering (ODAC) system uses a correlation-based similarity measure between time-series over a data stream. When turning a leaf into a node, the cluster is divided in two and new leaves start new computations. An agglomerative phase is used...
متن کاملODAC: Hierarchical Clustering of Time Series Data Streams
This paper presents a time series whole clustering system that incrementally constructs a tree-like hierarchy of clusters, using a top-down strategy. The Online Divisive-Agglomerative Clustering (ODAC) system uses a correlation-based dissimilarity measure between time series over a data stream and possesses an agglomerative phase to enhance a dynamic behavior capable of concept drift detection....
متن کاملPartitioning-Clustering Techniques Applied to the Electricity Price Time Series
Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract useful information from the electricity price time series. To be precise, two clustering techniques, K-means and Expectation Maximization, have been utilized for the analysis of ...
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ژورنال
عنوان ژورنال: Energies
سال: 2020
ISSN: 1996-1073
DOI: 10.3390/en13040924