Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Cost-based labeling of groups of mass spectra
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mass Spectrum Labeling: Theory and Practice
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Environmental Modelling & Software
Least squares quantization in PCM
IEEE Transactions on Information Theory
Environmental Modelling & Software
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Here we present a new open-source software package designed to facilitate the analysis of atmospheric data, with emphasis on data mining applications applied to single-particle mass spectrometry data from aerosol particles. The software package, Enchilada (Environmental Chemistry through Intelligent Atmospheric Data Analysis), is designed to seamlessly handle large datasets, to allow for temporal aggregation of data from many instruments, and to integrate techniques such as clustering (K-means, K-medians, and Art-2a), labeling of peaks in mass spectra, and temporal correlations of multiple datasets from multiple instrument types. The software, which continues to be developed and improved, provides users with a single package to integrate data from multiple mass spectrometer systems (ATOFMS, PALMS, SPASS, Q-AMS) as well as any time-based data stream. A detailed description of the software and examples of analysis methods that are incorporated into it are described here.