Name
Abstract | ||
|---|---|---|
•This paper presents a novel down-top incremental conceptual hierarchical text clustering approach using CFu-tree (ICHTC-CF) representation.•For summarizing a cluster, we use the term-based feature extraction in text clustering.•A new measure criterion, Comparison Variation (CV), is presented for judging whether the clusters can be merged or split.•The incremental clustering method is not sensitive to the input data order.•Experimental results show that the performance of our method outperforms k-means, which indicate our new technique is efficient and feasible. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.asoc.2014.11.015 | Applied Soft Computing |
Keywords | Field | DocType |
Text clustering,CFu-tree,Comparison Variation (CV),Incremental hierarchical clustering | Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Conceptual clustering,Cluster analysis,Single-linkage clustering,Hierarchical clustering,Complete-linkage clustering,Pattern recognition,Correlation clustering,Machine learning | Journal |
Volume | Issue | ISSN |
27 | C | 1568-4946 |
Citations | PageRank | References |
5 | 0.44 | 34 |
Authors | ||
2 | ||