大数据子空间聚类
2021-01-27 科研项目
•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