Summary of On Time Series Clustering with K-means, by Christopher Holder et al.
On time series clustering with k-means
by Christopher Holder, Anthony Bagnall, Jason Lines
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to standardize and improve time series clustering using a modified version of k-means. The proposed model incorporates a specialized distance function that considers time dependencies, making it more effective for clustering tasks. By adopting an end-to-end approach, the algorithm eliminates variability in initialisation and stopping criteria, allowing for fair comparisons between different Lloyd’s-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series clustering is a way to group similar patterns in data over time. Right now, there are many different ways to do this, but each method has its own strengths and weaknesses. The authors of this paper want to create a more standardized approach that combines the best parts of existing methods. They’re trying to make it easier to compare how well different methods work by using the same rules for all of them. |
Keywords
» Artificial intelligence » Clustering » K means » Time series
