An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data

By Liping Jing, Michael Kwok-Po Ng, Joshua Zhexue Huang. IEEE Transactions on Knowledge and Data Engineering, 2007

641 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/713ee269caf43efedc223517c944c625adb20a3c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dinh Q. Phung
2. B. Vo
3. Quang Nhat Tran
4. Raihan Seraj
5. Syed Mohammed Shamsul Islam
6. Mohiuddin Ahmed
7. Jason L. Williams
8. Hung Gia Hoang

In Computer Vision and Pattern Recognition

1. Raihan Seraj
2. B. Vo
3. Dinh Q. Phung
4. Quang Nhat Tran
5. Syed Mohammed Shamsul Islam
6. Mohiuddin Ahmed
7. Ian David Reid
8. Javen Qinfeng Shi

In Signal Processing

1. Raihan Seraj
2. B. Vo
3. Dinh Q. Phung
4. Syed Mohammed Shamsul Islam
5. Mohiuddin Ahmed

Citations in the corpus

• The k-means Algorithm: A Comprehensive Survey and Performance Evaluation (2020, methodology): “In [66], an entropy weighting k-means (EWKM) clustering algorithm was presented for clustering sparse datasets with high dimensions.”
• Model-Based Multiple Instance Learning (2017, background): “Each sparse observation is an array with most elements taking the same value(s) [22], [23] and can be represented as a ‘bag-of-features’ when the constituent features are not ordered, or as a linked-list when the constituent features are ordered.”