Sep-25-2019, 10:20 PM

Hello Fellow Scientists

i am kinda confused on this project, so I said let me ask here.

I have a dataset csv with with 30001 row and 14 columns starts with time 08/03/2018 1:00 to 05/01/2019 13:26

i need to use two or more clustering algorithms, build an unsupervised time-series classifier to identify characteristic day-length patterns in the attached data. Note that each of the columns in the provided data set includes sensor measurements of the same kind for light in a room (units in Lux).

using appropriate quantitative metrics to determine the number of time series clusters and to evaluate their quality.

Optional: In light of the data and the differences between algorithms, speculate on why a given method yielded quantitatively better clusters.

Code can be written in Python or R but any other language is allowed as long as i provide the code.

Interactive notebooks (like Jupyter) including the code, comments and visualizations are preferred.

Thank You indeed

i am kinda confused on this project, so I said let me ask here.

I have a dataset csv with with 30001 row and 14 columns starts with time 08/03/2018 1:00 to 05/01/2019 13:26

i need to use two or more clustering algorithms, build an unsupervised time-series classifier to identify characteristic day-length patterns in the attached data. Note that each of the columns in the provided data set includes sensor measurements of the same kind for light in a room (units in Lux).

using appropriate quantitative metrics to determine the number of time series clusters and to evaluate their quality.

Optional: In light of the data and the differences between algorithms, speculate on why a given method yielded quantitatively better clusters.

Code can be written in Python or R but any other language is allowed as long as i provide the code.

Interactive notebooks (like Jupyter) including the code, comments and visualizations are preferred.

Thank You indeed