HierArchical-Grid CluStering Based on DaTA Field in Time-Series and the Influence of the First-Order Partial Derivative Potential Value for the ARIMA-Model
| Autor: | Jing Geng, Krid Jinklub |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
Data field 02 engineering and technology Function (mathematics) computer.software_genre Grid Set (abstract data type) Knowledge extraction 020204 information systems 0202 electrical engineering electronic engineering information engineering Partial derivative 020201 artificial intelligence & image processing Data mining Autoregressive integrated moving average Cluster analysis computer |
| Zdroj: | Advanced Data Mining and Applications ISBN: 9783030050894 ADMA |
| DOI: | 10.1007/978-3-030-05090-0_3 |
| Popis: | Extend the function of static-time dataframe clustering algorithm (HASTA: HierArchical-grid cluStering based on daTA field) to be able to cluster the time-series dataframe. The algorithm purposed to use a set of “first-partial derivative potential value” given from HASTA in the multiple dataframes as the input to the autoregressive integrated moving average (ARIMA) under preliminary parameters. The ARIMA model could perform the pre-labeling task for the cluster(s) in the connected dataframe on the same time-series data. Calculating the structural similarity as a distance measure between timeframe, ARIMA would mark the high potential grid(s) as the cluster tracker. As the result, the ARIMA model could interpreting and reasoning cluster movement phenomena in the systematic approach. This integration is the attempt to show the influent power of data field in the term of knowledge representation. |
| Databáze: | OpenAIRE |
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