Mass spectrum labeling: theory and practice

We introduce the problem of labeling a particle's mass spectrum with the substances it contains, and develop several formal representations of the problem, taking into account practical complications such as unknown compounds and noise. This task is currently a bottle-neck in analyzing data from a new generation of instruments for real-time environmental monitoring.

[1]  G. Nemhauser,et al.  Integer Programming , 2020 .

[2]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[3]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[4]  K. Prather,et al.  Real-time characterization of individual aerosol particles using time-of-flight mass spectrometry , 1994 .

[5]  Jude W. Shavlik,et al.  Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..

[6]  R. Agarwal Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[7]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[8]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[9]  K. Prather,et al.  Real-Time Measurement of Correlated Size and Composition Profiles of Individual Atmospheric Aerosol Particles , 1996 .

[10]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[11]  B. Morrical,et al.  Real-Time Analysis of Individual Atmospheric Aerosol Particles: Design and Performance of a Portable ATOFMS , 1997 .

[12]  K. Prather,et al.  Mass spectrometry of aerosols. , 1999, Chemical reviews.

[13]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[14]  Kenneth A. Smith,et al.  Development of an Aerosol Mass Spectrometer for Size and Composition Analysis of Submicron Particles , 2000 .

[15]  Nie Yong Mining quantitative association rules , 2000 .

[16]  John McCarthy Phenomenal data mining , 2000, CACM.

[17]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[18]  S. Benson A Limited Memory Variable Metri Method in Subspa es and Bound Constrained Optimization Problems , 2001 .

[19]  Glenn Fung,et al.  Knowledge-Based Support Vector Machine Classifiers , 2002, NIPS.

[20]  Arindam Banerjee,et al.  Semi-supervised Clustering by Seeding , 2002, ICML.

[21]  Zheng Huang,et al.  Cost-based labeling of groups of mass spectra , 2004, SIGMOD '04.

[22]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.