I have i have I havea dataset likethat :
users T1 T2 … Tn
1 [1,2,1.5] [1,3,3] … [2,2,6]
2 [1,5,1.5] [1,3,4] … [2,8,6]
.
n [1,5,7.5] [5,3,4] … [2,9,6]
Given that lists are distinct incident change by time.
My aim to find distinct incidents which might happen to users by time.
I thought of feeding the full dataset to clustering algorithms , but I need an advice from you about best algorithms to fit such 2D dataset or best approach to follow in solving this problem
Dear Lubna Mahmoud Abu Zohair ,
Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.
https://www.hindawi.com/journals/tswj/2014/562194/
Regards,
Shafagat
BIRCH Clustering (BIRCH is short for Balanced Iterative Reducing and Clustering using Hierarchies) involves constructing a tree structure from which cluster centroids are extracted.
The answer by Shafagat Mahmudova is very good.
There are several time series clustering Python packages available to clone from Git sites: River (https://github.com/online-ml/river-extra) and Sktime (https://www.sktime.org/en/stable/) are the two most active projects out of around 20 that I have been tracking. Sktime is currently only batch clustering; River has some incremental clustering capabilities. Sktime has incorporated other time series analytics packages such as tsfresh, tslearn, stumpy and others into their code.
Sktime is under development by the Alan Turing Institute.
Thank you all for your advice.
I will definitely take it in to consideration and investigate it further, and if I'll end up on some relevant answers i will share it in this thread.
Thank you one more time.
Kind Regards,
Lubna
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