A Knowledge-based Data Reduction for Very Large Spatio-Temporal Datasets

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Title: A Knowledge-based Data Reduction for Very Large Spatio-Temporal Datasets
Authors: Le-Khac, Nhien-An
Bue, Martin
Whelan, Michael
Kechadi, Tahar
Permanent link: http://hdl.handle.net/10197/7852
Date: 21-Nov-2010
Online since: 2016-09-02T11:40:35Z
Abstract: Today, huge amounts of data are being collected with spatial and temporal components from sources such as metrological, 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 spatiotemporal data. We also present and discuss preliminary results of this approach.
Type of material: Conference Publication
Keywords: Spatio-temporal datasetsData reductionCentre-based clusteringDensity-based clusteringShared nearest neighbours
Other versions: http://adma2010.cqu.edu.cn/index.htm
Language: en
Status of Item: Peer reviewed
Conference Details: 6th International Conference on Advanced Data Mining and Applications, (ADMA 2010), Chongqing, China, 19-21 November 2010
Appears in Collections:Computer Science Research Collection

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