A Knowledge-based Data Reduction for Very Large Spatio-Temporal Datasets
|Title:||A Knowledge-based Data Reduction for Very Large Spatio-Temporal Datasets||Authors:||Le-Khac, Nhien-An
|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 datasets; Data reduction; Centre-based clustering; Density-based clustering; Shared 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|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.