Name
Abstract | ||
|---|---|---|
Clustering on XML documents is an important task. However, it is difficult to select the appropriate parameters’ value for the clustering algorithms. By integrating outlier detection with clustering, the paper takes a new approach for analyzing the XML documents by structure distance. After stating the XML tree distance, the paper proposes a new clustering algorithm, which stops clustering automatically by utilizing the outlier information and needs only one parameter, whose appropriate value range can be decided in the outlier mining process. The paper adopts the XML dataset with different structure and other real-life datasets to compare it with other clustering algorithms. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11610496_18 | APWeb Workshops |
Keywords | Field | DocType |
outlier detection,xml document | Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,XML tree,Constrained clustering,Cluster analysis,Database | Conference |
Volume | Issue | ISSN |
3842 LNCS | null | 0302-9743 |
ISBN | Citations | PageRank |
3-540-31158-0 | 1 | 0.35 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lv Tianyang | 1 | 33 | 8.49 |
| Xi-zhe Zhang | 2 | 38 | 8.94 |
| Wanli Zuo | 3 | 342 | 42.73 |
| Zhengxuan Wang | 4 | 47 | 13.93 |