An ensemble clustering model for mining concept drifting stream data in emergency management

Authors:
Yong Zhang;Yi Peng;Jun Li;Gang Kou;Yong Shi
Affiliations:
University of Electronic Science and Technology of China, Chengdu, P.R. China;University of Electronic Science and Technology of China, Chengdu, P.R. China;University of Electronic Science and Technology of China, Chengdu, P.R. China;University of Electronic Science and Technology of China, Chengdu, P.R. China;University of Nebraska at Omaha, Omaha, NE
Venue:
DM-IKM '12 Proceedings of the Data Mining and Intelligent Knowledge Management Workshop
Year:
2012

Citing 13
Cited 0

Learning in the presence of concept drift and hidden contexts

Machine Learning
Fast and effective text mining using linear-time document clustering

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster validity methods: part I

ACM SIGMOD Record
Clustering validity checking methods: part II

ACM SIGMOD Record
Mining concept-drifting data streams using ensemble classifiers

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical Clustering Algorithms for Document Datasets

Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Online clustering of parallel data streams

Data & Knowledge Engineering
Model-based evaluation of clustering validation measures

Pattern Recognition
Pattern Recognition, Fourth Edition

Pattern Recognition, Fourth Edition
Relative clustering validity criteria: A comparative overview

Statistical Analysis and Data Mining
Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams

ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining

Quantified Score

Hi-index	0.00

Visualization

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.