Efficient Clustering of Very Large Document Collections
By Inderjit S. Dhillon, James Fan, Yuqiang Guan. 2001
300 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/764fb39acf057b52f9e1221f630e701ff56ea3b9
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
- Brian Kulis
- Joydeep Ghosh
- Inderjit S. Dhillon
- Arindam Banerjee
- Trevor Darrell
- Pavel Berkhin
- Kate Saenko
- Suvrit Sra
In Signal Processing
- Pavel Berkhin
- Joydeep Ghosh
- Arindam Banerjee
- Inderjit S. Dhillon
- Subramanyam Mallela
- Dharmendra S. Modha
- Rong Yan
- Brian Kulis
Citations in the corpus
- Information-theoretic co-clustering (2003, methodology): “To initialize document clustering we use a random perturbation of the “mean” document, a strategy that has been observed to work well for document clustering [5].”
- Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (2005, methodology): “Further, the spherical kmeans (spkmeans) algorithm (Dhillon and Modha, 2001), that performs kmeans using cosine similarity instead of Euclidean distortion, has been found to work well for text clustering.”
- A Survey of Clustering Data Mining Techniques (2006, methodology): “…in data mining was brought to life by intense developments in information retrieval and text mining [Cutting et al. 1992; Steinbach et al. 2000; Dhillon et al. 2001], spatial database applications, for example, GIS or astronomical data, [Xu et al. 1998; Sander et al. 1998; Ester et al. 2000],…”
- Kernel k-means: spectral clustering and normalized cuts (2004, methodology): “To speed up the distance computation in our weighted kernel k-means algorithm, we can adapt the pruning procedure used in [4].”
- Co-clustering documents and words using bipartite spectral graph partitioning (2001, background): “In addition, the measures of purity and entropy are easily derived from the confusion matrix[6].”
- Dual Fuzzy-Possibilistic Co-clustering for Document Categorization (2007, methodology): “We utilized the Matrix Creation (MC) toolkit [4] to preprocess the documents.”
- Frequency-sensitive competitive learning for scalable balanced clustering on high-dimensional hyperspheres (2004, methodology): “The performance of an approach (and of a specific method within that approach) is quite data dependent; there is no clustering method that works the best across all types of data distributions.”