@inproceedings{495f5e47cd34402cb674ce4803f06f2e,
title = "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",
abstract = "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.",
keywords = "ARIMA, Data field, Knowledge discovery, Pattern recognition, Time-series, Time-series clustering",
author = "Krid Jinklub and Jing Geng",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 14th International Conference on Advanced Data Mining and Applications, ADMA 2018 ; Conference date: 16-11-2018 Through 18-11-2018",
year = "2018",
doi = "10.1007/978-3-030-05090-0\_3",
language = "English",
isbn = "9783030050894",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "31--41",
editor = "Guojun Gan and Xue Li and Shuliang Wang and Bohan Li",
booktitle = "Advanced Data Mining and Applications - 14th International Conference, ADMA 2018, Proceedings",
address = "Germany",
}