Dear Reseachers,
We have time sequence data for multiple binary signals in digital system and we wanted to cluster them based on time sequence binary data using machine learning
What approuch we should use?
Please guide.
Thanks,
Regards,
Viraj Rawal
Viraj Y Rawal Clustering binary time sequence data with machine learning is possible by combining techniques from time series analysis and machine learning. Here are some general approaches to clustering your binary time sequence data:
1. Preprocessing is the process of cleaning, standardizing, and altering data as needed. You may also need to undertake feature engineering, such as extracting time-based characteristics such as rolling means, standard deviations, and so on, depending on the nature of the data.
2. Time series representation: Represent time series data in such a way that it can be fed into a machine learning model. One of the most frequent ways to display a time series is to utilize a sliding window technique.
Dear Viraj Y Rawal ,
As technology evolved over time, the amount of data collected in the world had increased exponentially too. Big data such as building information models, parking transactions, and public transport transactions are rich and large datasets. However, existing applications of it tend to be very focused on specific use cases.
https://medium.com/towards-data-science/time-series-clustering-deriving-trends-and-archetypes-from-sequential-data-bb87783312b4
Regards,
Shafagat
- Viraj Y Rawal Time-series clustering: One approach is to use time-series clustering algorithms, such as dynamic time warping (DTW) or hidden Markov models (HMMs), to identify patterns in the binary time sequence data. These algorithms can be used to group similar sequences together and identify clusters in the data.
- Viraj Y Rawal Binary encoding: Another approach is to first convert the binary time sequence data into a numerical representation, such as a binary vector, and then use traditional clustering algorithms, such as k-means or hierarchical clustering, to group the sequences based on their numerical representation.
- Viraj Y Rawal Recurrent neural network (RNN): A Recurrent Neural Network could be used for this purpose, where each sequence is processed by a RNN, which then produces a fixed-length vector representation of the sequence, which is then passed through a clustering algorithm such as k-means to cluster the sequences.
- Viraj Y Rawal Autoencoder: Autoencoder is another neural network architecture that could be used in this case, where the autoencoder learns to compress the input data into a lower-dimensional representation, and then cluster the compressed representations using traditional clustering algorithms.
- Viraj Y Rawal Self-organizing maps (SOM): Self-organizing maps (SOM) is another neural network architecture that could be used to cluster the binary time sequence data. SOMs are trained to learn the topology of the data and group similar sequences together.
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