Summary of Cdc: a Simple Framework For Complex Data Clustering, by Zhao Kang et al.
CDC: A Simple Framework for Complex Data Clustering
by Zhao Kang, Xuanting Xie, Bingheng Li, Erlin Pan
First submitted to arxiv on: 6 Mar 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 The proposed framework for complex data clustering (CDC) efficiently processes different types of data with linear complexity, handling multi-view, non-Euclidean, and multi-relational datasets. The method utilizes graph filtering to fuse geometry structure and attribute information, then reduces complexity with adaptively learned anchors using a novel similarity-preserving regularizer. CDC is illustrated theoretically and experimentally, including deployment on a large-scale graph dataset of 111M size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way to group similar things together in complex data. This is important because we are collecting more and more data all the time, and it’s hard to make sense of it all. The new method can handle different types of data and is very efficient. It works by combining information about how the data is structured with information about what the data looks like. Then, it uses special “anchors” to help group similar things together. The researchers tested their method on a large dataset and showed that it works well. |
Keywords
* Artificial intelligence * Clustering
