Abstract
The paper presents a graph model and an agglomerative algorithm for text document clustering. Given a set of documents, the associations among frequently co-occurring terms in any of the documents naturally form a graph, which can be decomposed into connected components at various levels. Each connected component represents a concept in the collection. These concepts can categorize documents into different semantic classes. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as k-means, principal direction division partitioning, AutoClass and hierarchical clustering.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1556-1563 |
| Number of pages | 8 |
| ISBN (Print) | 9781479999255 |
| DOIs | |
| Publication status | Published - Dec 22 2015 |
| Event | 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States Duration: Oct 29 2015 → Nov 1 2015 |
Other
| Other | 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 |
|---|---|
| Country | United States |
| City | Santa Clara |
| Period | 10/29/15 → 11/1/15 |
Fingerprint
Keywords
- agglomerative document categorization/clustering
- association rules
- hierarchical clustering
- hypergraph
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Information Systems
- Software
Cite this
Agglomerative algorithm to discover semantics from unstructured big data. / Chiang, I-Jen.
Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1556-1563 7363920.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Agglomerative algorithm to discover semantics from unstructured big data
AU - Chiang, I-Jen
PY - 2015/12/22
Y1 - 2015/12/22
N2 - The paper presents a graph model and an agglomerative algorithm for text document clustering. Given a set of documents, the associations among frequently co-occurring terms in any of the documents naturally form a graph, which can be decomposed into connected components at various levels. Each connected component represents a concept in the collection. These concepts can categorize documents into different semantic classes. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as k-means, principal direction division partitioning, AutoClass and hierarchical clustering.
AB - The paper presents a graph model and an agglomerative algorithm for text document clustering. Given a set of documents, the associations among frequently co-occurring terms in any of the documents naturally form a graph, which can be decomposed into connected components at various levels. Each connected component represents a concept in the collection. These concepts can categorize documents into different semantic classes. The experiments on three different data sets from news, Web, and medical literatures have shown our algorithm is significantly better than traditional clustering algorithms, such as k-means, principal direction division partitioning, AutoClass and hierarchical clustering.
KW - agglomerative document categorization/clustering
KW - association rules
KW - hierarchical clustering
KW - hypergraph
UR - http://www.scopus.com/inward/record.url?scp=84963745319&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963745319&partnerID=8YFLogxK
U2 - 10.1109/BigData.2015.7363920
DO - 10.1109/BigData.2015.7363920
M3 - Conference contribution
AN - SCOPUS:84963745319
SN - 9781479999255
SP - 1556
EP - 1563
BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -