A Clustering-Based Data Reduction for Very Large Spatio-Temporal Datasets

摘要：

Today, huge amounts of data are being collected with spatial and temporal components from sources such as meteorological, satellite imagery etc. Efficient visualisation as well as discovery of useful knowledge from these datasets is therefore very challenging and becoming a massive economic need. Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data. Furthermore, data mining techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. As a consequence, instead of dealing with a large size of raw data, we can use these representatives to visualise or to analyse without losing important information. This paper presents a new approach based on different clustering techniques for data reduction to help analyse very large spatio-temporal data. We also present and discuss preliminary results of this approach.

关键词： spatio-temporal datasets ata reduction centre-based clustering ensity-based clustering shared nearest neighbours

作者: Nhien-An Le-Khac Martin Bue Michael Whelan M-Tahar Kechadi

作者单位: School of Computer Science and Informatics, University College Dublin, Belfield,Dublin 4, Ireland Ecole Polytechnique Universitaire de Lille, Villeneuve dAscq cedex, France School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 43-54

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）