Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings |
| Untertitel | 8th IEEE International Conference on Data Mining, ICDM 2008 |
| Seiten | 123-132 |
| Seitenumfang | 10 |
| Publikationsstatus | Veröffentlicht - Dez. 2008 |
| Veranstaltung | 8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italien Dauer: 15 Dez. 2008 → 19 Dez. 2008 |
Publikationsreihe
| Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
|---|---|
| ISSN (Print) | 1550-4786 |
Abstract
In the past few years there has been increased research interest in detecting previously unidentified events from Web resources. Our focus in this paper is to detect events from the click-through data generated by Web search engines. Existing event detection algorithms, which mainly study the news archive data, cannot be employed directly because of the following two unique features of click-through data: 1) the information provided by click-through data is quite limited; 2) not every query issued to a Web search engine corresponds to an event in the real world. In this paper, we address this problem by proposing an effective algorithm which Detects Events from ClicK-through data (DECK). We firstly transform click-through data to the 2D polar space by considering the semantic dimension and temporal dimension of queries. Robust subspace estimation is performed to detect subspaces such that each subspace consists of queries of similar semantics. Next, we prune uninteresting subspaces which do not contain queries corresponding to real events by simultaneously considering the respective distribution of queries along the semantic dimension and the temporal dimension in each subspace. Finally, events are detected from interesting subspaces using a nonparametric clustering technique. Compared with an existing approach, our experimental results based on real-life data have shown that the proposed approach is more accurate and effective in detecting real events from click-through data.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Allgemeiner Maschinenbau
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Proceedings: 8th IEEE International Conference on Data Mining, ICDM 2008. 2008. S. 123-132 4781107 (Proceedings - IEEE International Conference on Data Mining, ICDM).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - DECK
T2 - 8th IEEE International Conference on Data Mining, ICDM 2008
AU - Chen, Ling
AU - Hu, Yiqun
AU - Nejdl, Wolfgang
PY - 2008/12
Y1 - 2008/12
N2 - In the past few years there has been increased research interest in detecting previously unidentified events from Web resources. Our focus in this paper is to detect events from the click-through data generated by Web search engines. Existing event detection algorithms, which mainly study the news archive data, cannot be employed directly because of the following two unique features of click-through data: 1) the information provided by click-through data is quite limited; 2) not every query issued to a Web search engine corresponds to an event in the real world. In this paper, we address this problem by proposing an effective algorithm which Detects Events from ClicK-through data (DECK). We firstly transform click-through data to the 2D polar space by considering the semantic dimension and temporal dimension of queries. Robust subspace estimation is performed to detect subspaces such that each subspace consists of queries of similar semantics. Next, we prune uninteresting subspaces which do not contain queries corresponding to real events by simultaneously considering the respective distribution of queries along the semantic dimension and the temporal dimension in each subspace. Finally, events are detected from interesting subspaces using a nonparametric clustering technique. Compared with an existing approach, our experimental results based on real-life data have shown that the proposed approach is more accurate and effective in detecting real events from click-through data.
AB - In the past few years there has been increased research interest in detecting previously unidentified events from Web resources. Our focus in this paper is to detect events from the click-through data generated by Web search engines. Existing event detection algorithms, which mainly study the news archive data, cannot be employed directly because of the following two unique features of click-through data: 1) the information provided by click-through data is quite limited; 2) not every query issued to a Web search engine corresponds to an event in the real world. In this paper, we address this problem by proposing an effective algorithm which Detects Events from ClicK-through data (DECK). We firstly transform click-through data to the 2D polar space by considering the semantic dimension and temporal dimension of queries. Robust subspace estimation is performed to detect subspaces such that each subspace consists of queries of similar semantics. Next, we prune uninteresting subspaces which do not contain queries corresponding to real events by simultaneously considering the respective distribution of queries along the semantic dimension and the temporal dimension in each subspace. Finally, events are detected from interesting subspaces using a nonparametric clustering technique. Compared with an existing approach, our experimental results based on real-life data have shown that the proposed approach is more accurate and effective in detecting real events from click-through data.
UR - http://www.scopus.com/inward/record.url?scp=67049098638&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2008.78
DO - 10.1109/ICDM.2008.78
M3 - Conference contribution
AN - SCOPUS:67049098638
SN - 9780769535029
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 123
EP - 132
BT - Proceedings
Y2 - 15 December 2008 through 19 December 2008
ER -