Name
Abstract | ||
|---|---|---|
A novel similarity measure based on spatial overlapping relation is proposed in this paper, which calculates the similarity between a pair of data points by using the mutual overlapping relation between them in a multi-dimensional space. A spatial overlapping based hierarchical clustering method SOHC was also developed and implemented aimed to justify the effectiveness of the proposed similarity measure. SOHC works well both in low-dimensional and high-dimensional datasets, and is able to cluster arbitrary shape of clusters. Moreover, it can work for both numerical and categorical attributes in a uniform way. Experimental results carried out on some public datasets collected from the UCI machine learning repository and predictive toxicology domain show that SOHC is a promising clustering method in data mining. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.379 | FSKD (2) |
Keywords | Field | DocType |
hierarchical clustering,data mining,similarity measure,promising clustering method,public datasets,high-dimensional datasets,hierarchical clustering method,data point,novel similarity measure,mutual overlapping relation,proposed similarity measure,spatial overlapping relation,machine learning,merging,couplings,database management systems,learning artificial intelligence,data points | Cluster (physics),Data mining,Similarity measure,Computer science,Categorical variable,Artificial intelligence,Merge (version control),Cluster analysis,Data point,Hierarchical clustering,Distance measurement,Pattern recognition,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Hong | 1 | 21 | 11.66 |
| Gongde Guo | 2 | 1 | 0.35 |
| Yu Huang | 3 | 585 | 33.35 |
| Tian-qiang Huang | 4 | 33 | 5.74 |