Name
Abstract | ||
|---|---|---|
An effective XML cluster method called neighbor center clustering algorithm (NCC) is presented in this paper, whose similarity is obtained through both structural and content information contained in XML files. Structural similarity is measured by the idea of Longest Common Subsequence, while content similarity is achieved using TF-IDF principles. It reduces computation complexity by avoiding direct search for cluster centers. Experiments show that the NCC can obtain high purity and F-measure value and is suitable and applicable for clustering XML with both homogenous and heterogeneous structures. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/PAAP.2010.55 | PAAP |
Field | DocType | ISBN |
Data mining,Text mining,Longest common subsequence problem,XML,Computer science,Direct search,Feature extraction,Cluster analysis,Computation complexity,Computational complexity theory | Conference | 978-1-4244-9482-8 |
Citations | PageRank | References |
1 | 0.35 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Piao | 1 | 1 | 0.69 |
| Chen Liu | 2 | 29 | 11.46 |
| Xiu-kun Wang | 3 | 45 | 8.99 |