A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-scale difference equations II. local regularity, infinite products of matrices and fractals
SIAM Journal on Mathematical Analysis
Essential wavelets for statistical applications and data analysis
Essential wavelets for statistical applications and data analysis
Estimating the square root of a density via compactly supported wavelets
Computational Statistics & Data Analysis
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Issues in data stream management
ACM SIGMOD Record
Wavelet density estimators over data streams
Proceedings of the 2005 ACM symposium on Applied computing
Wavelet density estimators over data streams
Proceedings of the 2005 ACM symposium on Applied computing
A framework for estimating complex probability density structures in data streams
Proceedings of the 17th ACM conference on Information and knowledge management
Nonparametric density estimation of streaming data using orthogonal series
Computational Statistics & Data Analysis
Density estimation for spatial data streams
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
A fast and recursive algorithm for clustering large datasets with k-medians
Computational Statistics & Data Analysis
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There has been an important emergence of applications in which data arrives in an online time-varying fashion (e.g. computer network traffic, sensor data, web searches, ATM transactions) and it is not feasible to exchange or to store all the arriving data in traditional database systems to operate on it. For this kind of applications, as it is for traditional static database schemes, density estimation is a fundamental block for data analysis. A novel online approach for probability density estimation based on wavelet bases suitable for applications involving rapidly changing streaming data is presented. The proposed approach is based on a recursive formulation of the wavelet-based orthogonal estimator using a sliding window and includes an optimised procedure for reevaluating only relevant scaling and wavelet functions each time new data items arrive. The algorithm is tested and compared using both simulated and real world data.