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ADaM version 4.0 (Eagle) Tutorial

ADaM version 4.0 (Eagle) Tutorial. Information Technology and Systems Center University of Alabama in Huntsville. Tutorial Outline . Overview of the Mining System Architecture Data Formats Components Using the client: ADaM Plan Builder Demos How to write a mining plan.

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ADaM version 4.0 (Eagle) Tutorial

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  1. ADaM version 4.0(Eagle)Tutorial Information Technology and Systems Center University of Alabama in Huntsville ITSC/University of Alabama in Huntsville

  2. Tutorial Outline • Overview of the Mining System • Architecture • Data Formats • Components • Using the client: ADaM Plan Builder • Demos • How to write a mining plan ITSC/University of Alabama in Huntsville

  3. ADaM v4.0 Architecture • Simple component based architecture • Each operation is a stand alone executable • Users can either use the PlanBuilder or write scripts using their favorite scripting language (Perl, Python, etc) • Users can write custom programs using one or more of the operations • Users can create webservices using these operations ITSC/University of Alabama in Huntsville

  4. Versatile/Reusable Mining Component Architecture of ADaM v4.0 (Eagle) Exploration/Interactive Applications Production/Batch Interface(s) Custom Program ADaM PLAN BUILDER E E A1 A3 A` E Distributed Access Driver Program DP Web Service Interface WS ESML Description E Virtual Repository of Operations 3rd Party ……… DP DP DP WS WS DP WS DP WS WS E A1 A2 A3 An A` E E E E ADaM V4.0 ITSC/University of Alabama in Huntsville

  5. ADaM Data Formats • There are two data formats that work with ADaM Components • ARFF Format • An ARFF (Attribute-Relation File Format) file is an ASCII text file that describes a list of instances sharing a set of attributes • Binary Image Format • Used to write image files ITSC/University of Alabama in Huntsville

  6. ARFF Data Format • ARFF files have two distinct sections. The first section is the Header information, which is followed by the Data information. • The Header of the ARFF file contains the name of the relation, a list of the attributes (the columns in the data), and their types. An example header on the standard IRIS dataset looks like this: @RELATION iris @ATTRIBUTE sepallength NUMERIC @ATTRIBUTE sepalwidth NUMERIC @ATTRIBUTE petallength NUMERIC @ATTRIBUTE petalwidth NUMERIC @ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica} @DATA 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa ITSC/University of Alabama in Huntsville

  7. Binary Image Data Format • Contains a header with signature and size (X,Y,Z) followed by the image data • Sample code to write header: int header[4]; header[0] = 0xabcd; header[1] = mSize.x; header[2] = mSize.y; header[3] = mSize.z; if (fwrite (header, sizeof(int), 4, outfile) != 4) { fprintf (stderr, "Error: Could not write header to %s\n", filename); return(false); } ITSC/University of Alabama in Huntsville

  8. ADaM Components Components arranged into FOUR groups: • Image Processing (Binary Image format) • Contains typical image processing operations such as spatial filters • Pattern Recognition (ARFF format) • Contains pattern recognition and mining operations for both supervised and unsupervised classification • Optimization • Contains general purpose optimization operations such as genetic algorithms and stochastic hill climbing • Translation • Contains utility operations to convert data from one format to another such as image to gif ITSC/University of Alabama in Huntsville

  9. ADaM Mining Plan • A sequence of selected operations • The ADaM Plan Builder allows the user to select and sequence Mining Operations for a given problem • One could use any scripting language to write a mining plan Opn 3 Opn1 Opn 2 ITSC/University of Alabama in Huntsville

  10. ADaM Plan Builder – Layout Operation Menu contains the list of operations one can select • Plan Menu allows one to: • Create a new plan or Load an existing plan • Remove a newly-added operation from a plan ITSC/University of Alabama in Huntsville

  11. ADaM Plan Builder – Layout Panel where Mining Plan can be viewed either as a text or a tree ITSC/University of Alabama in Huntsville

  12. Description about the Operation can be viewed in this panel Panel where Mining Plan can be viewed either as text or a tree Sample values for Operation’s Parameters are show in this panel ADaM Plan Builder – Layout All the parameters needed for the Operation are described here ITSC/University of Alabama in Huntsville

  13. Go Mine the data using the Mining Plan Allows user to select the operation and add it to the Mining Plan ADaM Plan Builder – Layout Utility function to create samples for training ITSC/University of Alabama in Huntsville

  14. Demo! • Training a classifier to identify cancerous breast cells using a Bayes Classifier • Workflow: • Brief explanation on Bayes Classifier • Sampling the data (training and testing set) • Training the Bayes Classifier • Applying the Bayes Classifier • Interpretation of the Results ITSC/University of Alabama in Huntsville

  15. Bayes Classifier END POINT: BAYES THEOREM CLASSIFIER FOR SEGMENTATION STARTING POINT: BAYES THEOREM FOR CONDITIONAL PROBABILITY TERM 1: PROBABILITY OF DATA POINT X BELONGING IN CLASS ( I ) TERM 2: PROBABILITY OCCURRENCE OF A CLASS BASED ON NUMBER OF CLASSES USED IN SEGMENTATION TERM 3: NORMALIIZATION TERM TO KEEP VALUES BETWEEN 0 -1 TERM 4: PROBABILITY THAT DATA POINT X BELONGS TO CLASS (I) ITSC/University of Alabama in Huntsville

  16. Data File • Instances described by attributes and a class label (4 –cancerous, 2-non-cancerous) @relation breast_cancer @attribute Clump_Thickness real @attribute Uniformity_of_Cell_Size real @attribute Uniformity_of_Cell_Shape real @attribute Marginal_Adhesion real @attribute Single_Epithelial_Cell_Size real @attribute Bare_Nuclei real @attribute Bland_Chromatin real @attribute Normal_Nucleoli real @attribute Mitoses real @attribute class {2, 4} @data 5.000000 1.000000 1.000000 1.000000 2.000000 1.000000 3.000000 1.000000 1.000000 2 5.000000 4.000000 4.000000 5.000000 7.000000 10.000000 3.000000 2.000000 1.000000 2 ITSC/University of Alabama in Huntsville

  17. Demo! ITSC/University of Alabama in Huntsville

  18. Evaluating Results (Training Set) Confusion Matrix | 0 1 <--- Actual Class -------------------------------------- 0 | 214 3 1 | 14 110 ^ | +------ Classified As POD 0.973451 FAR 0.112903 CSI 0.866142 HSS 0.890194 Accuracy 324 of 341 (95.014663 Pct) Probability of Detection False Alarm Rate Skill Scores Overall Accuracy based on Confusion Matrix ITSC/University of Alabama in Huntsville

  19. Evaluating Results (Test Set) Confusion Matrix | 0 1 <--- Actual Class -------------------------------------- 0 | 205 3 1 | 11 123 ^ | +------ Classified As POD 0.976190 FAR 0.082090 CSI 0.897810 HSS 0.913185 Accuracy 328 of 342 (95.906433 Pct) Probability of Detection False Alarm Rate Skill Scores Overall Accuracy based on Confusion Matrix ITSC/University of Alabama in Huntsville

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