Ten lectures on wavelets
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
Findout: finding outliers in very large datasets
Knowledge and Information Systems
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Parallel Implementation of Wavelet Transforms on Distributed-Memory Multicomputers
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
Detecting region outliers in meteorological data
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Detecting graph-based spatial outliers
Intelligent Data Analysis
A parallel multi-scale region outlier mining algorithm for meteorological data
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Parallel wavelet transform for spatio-temporal outlier detection in large meteorological data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
| Hi-index | 0.02 |
Wavelet analysis is a practical tool to study signal analysis and image processing. Traditional Fourier transform can also transfer the signal into frequency domain, but wavelet analysis is more attractive for its features of multi-resolution and localization of frequency. Recently, there has been significant development in the use of wavelet methods in the data mining process. However, the objective of the study described in this paper is twofold: designing a wavelet transform algorithm on the multiprocessor architecture and using this algorithm in mining spatial outliers of meteorological data. Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Outlier detection reveals important and valuable information from large spatial data sets. As region outliers are commonly multi-scale objects, wavelet analysis is an effective tool to study them. In this paper, we present a wavelet based approach and its applicability in outlier detection. We design a suite of algorithms to effectively discover region outliers and also a parallel algorithm is designed to bring efficiency and speedup for the wavelet analysis. The applicability and effectiveness of the developed algorithms are evaluated on real-world meteorological dataset.