Principal Direction Divisive Partitioning

By Daniel Boley. 1998

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

1. Tao Li
2. Pavel Berkhin
3. Chris H. Q. Ding
4. Xingcheng Ran
5. Ying Zhao
6. Haesun Park
7. Jie Chen
8. Wei Peng

Citations in the corpus

• Efficient phrase-based document indexing for Web document clustering (2004, methodology): “The second data set (DS2) is a collection of 2,340 Reuters news articles posted on Yahoo! news, and was used by Boley in [43], [44], [45].”
• Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection (2009, methodology): “In what follows two methods are proposed which are based on a spectral bisection (Boley, 1998; Juhász and Mályusz, 1980; Tritchler et al., 2005) of the graph obtained from an inexpensive Lanczos procedure (Lanczos, 1950).”
• A Survey of Clustering Data Mining Techniques (2006, methodology): “SVD application to hierarchical divisive clustering of document collections resulted in the PDDP (Principal Direction Divisive Partitioning) algorithm [Boley 1998].”
• Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions (2009, methodology): “and closely related to “Principal Direction Divisive Partitioning” algorithm used in data mining [ 40 ].”
• Evaluation of hierarchical clustering algorithms for document datasets (2002, methodology): “Related papers are available via WWW at URL: http://www.cs.umn.edu/ karypis Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that…”
• Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering (2004, methodology): “For some of these algorithms the criterion function is implicit (e.g., PDDP (Boley, 1998)), whereas for other algorithms (e.g, K -means (MacQueen, 1967), Cobweb (Fisher, 1987), and Autoclass (Cheeseman & Stutz, 1996)) the criterion function is explicit and can be easily stated.”
• Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “The Principal Direction Divisive Partitioning (PDDP) method (Boley 1998) is proposed for clustering document sets.”
• Orthogonal nonnegative matrix t-factorizations for clustering (2006, methodology): “This is from WebACE project and has been used for document clustering [2, 13].”
• How much can k-means be improved by using better initialization and repeats? (2019, methodology): “…linear projection-based techniques have been considered in the literature:  Diagonal axis [65]  Single axis [66, 67]  Principal axis [68, 69, 46, 70, 67, 71]  Two random points [72]  Furthest points [72] After the projection is performed, the points are partitioned into k equal size clusters…”
• Trace optimization and eigenproblems in dimension reduction methods (2011, methodology): “The line shown in this first figure shows how a method based on PCA (called PDDP, see [9]) partitions the set.”
• The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering (2006, methodology): “WebACE The K-dataset was from WebACE project and has been used for document clustering [6, 19].”