Neural (and non neural) tools for data mining in massive data sets
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1 Neural (and non neural) tools for data mining in massive data sets Giuseppe Longo & Astroneural Department of Physical Sciences University Federico II in Napoli I.N.F.N. Napoli Unit I.N.A.F. Napoli Unit January 2005, Edimburgh
2 The Astroneural Collaboration Astroneural DSF - DMI Giuseppe Longo DSF Gennaro Miele DSF Roberto Tagliaferri DMI Roberto Amato (DSF student) Angelo Ciaramella (DMI post Doc) Carmine Del Mondo (DSF fellow) Lara de Vinco (DMI fellow) Ciro Donalek (DSF Ph.D.).) Omar Laurino (DSF student) Gianpiero Mangano (INFN - senior) Giancarlo Raiconi (DMI senior) Antonio Staiano (DMI - post Doc) Dipartimento di Scienze Fisiche Università Federico II - Napoli Dip. di Matematica ed Informatica Università di Salerno
3 CAMPUS GRID
4 Dipartimento di Scienze Fisiche Locale 1G01 Sala dell infrastruttura GRID principale Armadio telematico infrastrutturale CDS GARR Dipartimento di Chimica Dipartimento di Matematica e Applicazioni
5 256 Itanium 2CPU s 1 GB RAM 16 (2*8) Alpha 512 Pentium IV CPU 128 Opteron Optical fiber backbone
6 There are hundreds of different NN s MLP (Multi layer perceptron): slow, supervised, non linear SOM (self organizing maps) : faster, unsupervised, non linear, great visualization, non physical output GTM (generative topographic mapping): slow, unsupervised, great visualization, physical output PCA & ICA linear and non linear: terrible visualization, physical output, good performances on uncorrelated data Fuzzy C Means: slow on MDSs, effective in fuzzy problems PPS: great (the best ones for unsupervised clustering, classification and visualization) Competitive Evolution on Data (CED): bad visualization, great accuracy as unsupervised clustering tool, Etc.
7 Fact: In VO data sets: D D >> 1, D S >> 1 Advantages: Data Complexity Multidimensionality Discoveries But: The computational cost of clustering analysis: K-means: K N I D Expectation Maximisation: K N I D 2 Monte Carlo Cross-Validation: M K max2 N I D 2 N = no. of data vectors, D = no. of data dimensions K = no. of clusters chosen, K max = max no. of clusters tried I = no. of iterations, M = no. of Monte Carlo trials/partitions Terascale (Petascale?) computing and/or better algorithms
8 The Curse of Hyperdimensionality Visualization A fundamental limitation of the human perceptionis that we cannot visualize spaces with dimensionality higher than: DMAX = 3? 5? 2-D 3-D WHAT DO WE DO WHEN N=50?
9 To visualize and understand we need somehow to compress the relevant information User DM Algorithm Visualization Interactive visualization is a key part of the data mining process: Some methodology exists, but much more is needed
10 Aims and applications of AstroNeural User friendly tool to perform clustering and data mining in high dimensionality spaces Aims Clustering & pattern recognition in high dimensionality spaces Visualization Classification Parametrization of images Modeling of massive data sets Applications Astrophysics Genetics Geophysics High energy physics Atmospheric physics Etc.
11 Neural Networks are good at: performing linear and non linear interpolation generalizing performing non linear analysis and itentify common trends in data for forecasting classifying A priori knowledge needed Training Computation of errors They learn in two main ways: Supervised Unsupervised Null/small a priori knowledge Clustering done on statistical properties of data themeselves Performances and errors are derived statistically Knowledge comes through labeling
12 Astroneural V1.01 open open import import Prototype in C ++ & Matlab partial DEMO on this Laptop compliant preprocessing header non compliant Head/proc. Parameter and training options supervised Supervised unsupervised unsupervised Parameter options Training set preparation Labeled unlabeled labeled Label preparation Feature selection via unsupervised clustering Feature selection via unsupervised clustering MLP RBF Fuzzy set Etc. SOM GTM PPS. Etc. INTERPRETATION
13
14 3-D U Matrix Similarity coloring Feature significance maps
15 Example n. I: photometric redshifts for SDSS SDSS-EDR DB Unsupervised SOMS s + supervised MLP s SOM unsup. completeness SOM unsup. Set construction Reliability Map MLP supervised experiments SOM supervised Feature selection Best MLP model Input data set: SDSS EDR photometric data (galaxies) Training/validation/test set: SDSS-EDR spectroscopic subsample
16 Unsupervised SOMS s + supervised MLP s Robust error: millions P.R. SOM output (each exhagon is a neuron) Numbers above frame: redshift range Numbers in the cells: number of input data activating that neuron
17 The Curse of Hyperdimensionality Visualization A fundamental limitation of the human perceptionis that we cannot visualize spaces with dimensionality higher than: DMAX = 3? 5? 2-D 3-D WHAT DO WE DO WHEN N=50?
18 Probabilistic Principal Surfaces (PPS) GOODS Data set
19 Astroneural Version 1.01 ready Portation on freeware software in progress Web interface ready Implementation of specific tasks for GRID use is in progress (NA, TS, CT, etc.) Implementation of backwards connection to pixels is in progress
20 B.E.S. U-Matrix 278 parameters Compressed feature space BMU matrices? UP: good tracking Below: bad tracking
21 Probabilistic Principal Surfaces (PPS) GOODS Data set
22 Astroneural Version 1.01 ready Portation on freeware software in progress Web interface ready Implementation of specific tasks for GRID use is in progress (NA, TS, CT, etc.) Implementation of backwards connection to pixels is in progress
23
24 Status Matlab Version 1.0 ready and available (no documentation) Porting on C++ in progress Web access (upload data and download results) done (to be redone) VO conversion (to be done) Integration with high performance visualization tools (VISIVO) beginning with CT/BO
25 VO started in 2000 VO Tech is a new project.. And we shall be judged for it Where are we? Where do we want to go Why Census of existing Sharing know how Choice of test applications Who is going to say whether they are correct and useful? Templates Simulations vs real data?
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