An ensemble clustering model for mining concept drifting stream data in emergency management
Authors: 
Yong Zhang, Yi Peng, Jun Li, Gang Kou, Yong ShiAuthors Info & Claims
Article No.: 2, Pages 1 - 8
Abstract
Mining data streams with concept drifts is always an important and challenge task for researchers in both application and theory areas, such as emergency management. Because of requiring massive training data with labels, it is a hard and time costing work for existing (ensemble) classical models, sometimes even impossible. Aim to resolve this issue, in this paper; we propose an ensemble clustering model for mining concept drifting stream data in emergency management. Motivated by classifiers, the model will mine the data in two steps: "training" and "testing", just with a small training set. According to the experiment, the results demonstrate the effect and performance of the proposed model in mining data streams with concept drifts.
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Information
Published In
August 2012
55 pages
ISBN:9781450315517
DOI:10.1145/2462130
- General Chair:
- Qiang Yang,
- Program Chairs:
- Deepak Agarwal,
- Jian Pei
Copyright © 2012 ACM.
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Published: 12 August 2012
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- Foxconn "ZhuoCai" Funds
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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 12 - 16, 2012
Beijing, China
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