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Chinese Virtual Observatory Zhang Yanxia China-VO Group 2006.11.30 in Guilin Outline • • • • • • Why What How Example challenge summary China-VO 2006, Guilin 11/29-12/03 2 Astronomy facing “data avalanche” Necessity Is the Mother of Invention DM&KDD China-VO 2006, Guilin 11/29-12/03 IRAS 25m 2MASS 2m DSS Optical IRAS 100mWENSS 92cmNVSS 20cm GB 6cm ROSAT ~keV 3 Issues in Astronomy Ofer Lahav, 2006, astro-ph/0610703 Summary on the 4th meeting on “Statistical Challenge in Modern Astronomy” held at Penn State University in June 2006 • Compression (e.g. Galaxy images and spectra) • Classification (e.g. Stars, galaxies, or Gamma Ray Bursts) • Reconstruction (e.g. of blurred galaxy images, mass distribution from weak gravitational lensing) • Feature extraction (e.g. signatures feature of stars, galaxies and quasars) • Parameter estimation (e.g. Star parameter measurement, Photometric redshift prediction, orbital parameters of extrasolar planets, or cosmological parameters ) • Model selection (e.g. are there 0,1,2,……planets around stars, or is there a cosmological model with none-zero neutrino mass more favorable) China-VO 2006, Guilin 11/29-12/03 4 Science Requirements for DM (Borne K D, 2001, Proc. Of the MPA/ESO/MPE Workshop,671) Cross-Identification - refers to the classical problem of associating the source list in one database to the source list in another. Cross-Correlation - refers to the search for correlations, tendencies, and trends between physical parameters in multi-dimensional data, usually across databases. Nearest-Neighbor Identification - refers to the general application of clustering algorithms in multi-dimensional parameter space, usually within a database. Systematic Data Exploration - refers to the application of the broad range of event-based and relationship-based queries to a database in the hope of making a serendipitous discovery of new objects or a new class of objects. China-VO 2006, Guilin 11/29-12/03 5 KDD: Opportunity and Challenges Competitive Pressure Data Rich Knowledge Poor (the resource) KDD Data Mining Technology Mature Enabling Technology (Interactive MIS, OLAP, parallel computing, Web, etc.) China-VO 2006, Guilin 11/29-12/03 6 KDD: A Definition KDD is the automatic extraction of non-obvious, hidden knowledge from large volumes of data. 106-1012 bytes: never see the whole data set or put it in the memory of computers Data mining algorithms? China-VO 2006, Guilin What knowledge? How to represent and use it? 11/29-12/03 7 Benefits of Knowledge Discovery Value Disseminate DSS Generate Volume MIS EDP Rapid Response EDP: Electronic Data Processing MIS: Management Information Systems DSS: Decision Support Systems China-VO 2006, Guilin 11/29-12/03 8 DM: A KDD Process – Data mining: the core of knowledge discovery process. Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases Selection Work at each process of DM 60 50 40 30 20 10 0 DM object Evalution Data preparation China-VO 2006, Guilin Data processing 11/29-12/03 Analysis and 10 Primary Tasks of Data Mining finding the description of several predefined classes and classify a data item into one of them. Classification ? maps a data item to a real-valued prediction variable. identifying a finite set of categories or clusters to describe the data. finding a model which describes significant dependencies between variables. Dependency Modeling Regression discovering the most significant changes in the data Deviation and change detection Clustering finding a compact description for a subset of data Summarization China-VO 2006, Guilin 11/29-12/03 11 Feature selection • • • • Filter method Wrapper method Embedded method Feature weighted method China-VO 2006, Guilin 11/29-12/03 12 Feature extraction • • • • • • • • • • • • • • • • • • • PCA Factor analysis (Principal FA/Maximum Likelihood FA) Projection pursuit ICA Non-linear PCA/ICA Random projection Principal curves MDS LLE ISOMAP Topological continuous map Neural network Vector quantization Kernel PCA/ICA LDA (linear discriminant analysis ) QDA (quadratic discriminant analysis) FDA (Fisher discriminant analysis) GDA (Generalized discriminant analysis) KDDA (kernel direct discriminant analysis) China-VO 2006, Guilin 11/29-12/03 13 Classification Methods • Based on statistical theory: SVMs, ML, LDA,FDA,QDA,KNN • Based on NN: LVQ, RBF, PNN, KSOM,BBN,SLP,MLP • Based on Decision Tree: REPTree, RandomTree, CART,C5.0, J48, DecisionStump, RandomForest, NBtree,AC2,Cal5, ADTree,KDTree • Based on Decision Rule: Decision Table,CN2,ITrule, AQ • Based on bayesian theory: Naive Bayes classifier, NBTree • Based on meta learning: adaboost, boosting, bagging • Based on evolution theory: genetic algorithm • Based on fuzzy theory: fuzzy set, rough set • Ensembles of classifiers Data Mining algorithm China-VO 2006, Guilin patterns 11/29-12/03 14 Regression Methods • • • • • • • • • • • • • • • • • • • (penalized) logistic regression Bayesian regression analysis Additive regression Locally weighted regression Voted perceptron network Projection pursuit regression Recursive partitioning regression Alternating condition expectation Stepwise regression Recursive least square Fourier transform regression Ruled-based regression Principal component regression Instance-based regression Multivariate adaptive regression splines Regression trees (CART, RETIS, M5,random forest, KDtree) Simple windowed regression SVM NN China-VO 2006, Guilin 11/29-12/03 15 Method to estimate errors • Train-test • Cross-validation • Bootstrap • Leave-one-out China-VO 2006, Guilin 11/29-12/03 16 Evaluation of methods • Accuracy • Speed • Comprehensibility • Time to learn • Generalization China-VO 2006, Guilin 11/29-12/03 17 Model Selection for Classifiction • Accuracy • G-mean • F-measure • ROC (Receive Operating Characteristic Curve) China-VO 2006, Guilin 11/29-12/03 18 Model Selection for Regression • AIC(Akaike information criterion) • BIC (Bayesian information criterion) • SRM (Structure Risk Minimization) China-VO 2006, Guilin 11/29-12/03 19 Example 1 Lim Jien-sien et al. Machine Learning, 40, 203-229(2000) 33 algorithms on 16 different samples 22 decision trees CART, S-Plus tree, C4.5,FACT,QUEST,IND,OC1,LMDT,CAL5,T1 9 statistical methods LDA,QDA,NN,LOG,FDA,PDA,MDA,POL 2 neural networks LVQ,RBF China-VO 2006, Guilin 11/29-12/03 20 Example 1 Lim Jien-sien et al. Machine Learning, 40, 203-229(2000) China-VO 2006, Guilin 11/29-12/03 21 Example 2 China-VO 2006, Guilin 11/29-12/03 22 Example 3 Zhao,Y, Zhang,Y., 2006, submitted to cospar China-VO 2006, Guilin 11/29-12/03 23 Example 3 Zhang,Y,Zhao,Y, 2006, submitted to CHJAA For NB, ADTree MLP, the corresponding whole accuracy amounts to 97.5%, 98.5% and 98.1%, respectively. China-VO 2006, Guilin 11/29-12/03 24 Example 4 Zhang,Y, Luo, A, Zhao,Y, 2006, submitted to Cospar By best-forward search, j-h, b-v,j+ 2.5lgFpeak are optimal features selected from the 10 features. Decision Table is applied. 10-fold cross-validation for training and test. 98.03% China-VO 2006, Guilin 11/29-12/03 25 Example 5 Li,Y.,Zhang,Y.,Zhao,Y.,2006,submitted to Chinese Science k-Nearest neighbor classifier China-VO 2006, Guilin 11/29-12/03 26 Example 6 Zhang,Y., Zhao, Y., 2006,ADASS XV,351,173 China-VO 2006, Guilin 11/29-12/03 27 Challenges and Influential Aspects Massive data sets, high dimensionality (efficiency, scalability) Different sources of data (distributed, heterogeneous databases, noise and missing, irrelevant data, etc.) Changing data and knowledge Handling of different types of data with different degree of supervision Interactive, Visualization Knowledge Discovery Understandability of patterns, various kinds of requests and results (decision lists, inference networks, concept hierarchies, etc.) China-VO 2006, Guilin 11/29-12/03 28 Summary • • • • • Linear or non-linear Gassian or non-gassian Continous or discrete Missing or not Comparision of the number of attributes with that of records • Choose the appropriate method or ensemble algorithms according to the task and data characteristics China-VO 2006, Guilin 11/29-12/03 29 Prospect With the wing of DM, find more, better or best knowledge! Thank you for your attention! China-VO 2006, Guilin 11/29-12/03 30 Thank you !!!