大数据子空间聚类

•Project description/goals

Large-scale subspace clustering via k-factorization

KDD’21 (The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining). 2021.

•Importance/impact, challenges/pain points

1.Clustering 100k data points in 1 minute

2.Online clustering

•Solution description

Group-sparse matrix factorization for clustering

•Key contribution/commercial implication

1.Linear time and space complexity

2.High-clustering accuracy on large-scale datasets

3.Handle outliers and missing values

•Team/contributors

Jicong Fan

•Numerical results

科研项目