Random Indexing K-tree
Information Retrieval
2010-02-02 v2 Artificial Intelligence
Data Structures and Algorithms
Abstract
Random Indexing (RI) K-tree is the combination of two algorithms for clustering. Many large scale problems exist in document clustering. RI K-tree scales well with large inputs due to its low complexity. It also exhibits features that are useful for managing a changing collection. Furthermore, it solves previous issues with sparse document vectors when using K-tree. The algorithms and data structures are defined, explained and motivated. Specific modifications to K-tree are made for use with RI. Experiments have been executed to measure quality. The results indicate that RI K-tree improves document cluster quality over the original K-tree algorithm.
Keywords
Cite
@article{arxiv.1001.0833,
title = {Random Indexing K-tree},
author = {Christopher M. De Vries and Lance De Vine and Shlomo Geva},
journal= {arXiv preprint arXiv:1001.0833},
year = {2010}
}
Comments
8 pages, ADCS 2009; Hyperref and cleveref LaTeX packages conflicted. Removed cleveref