Icon

KNIME_​thesis task_​v01

DATA PREPARATION AND ACCESS1. Read data from local storage2. Missing value treatment3. Normalize data4. Re-format error data by format transformation nodes 5. Overview by statistic calculation4. Filter out irrelevant attributes5. Find the method to assign Opitz code position for next analysis step DATA ANALYSIS AND MINING1. Calculate min/ max/ mean values from numerical data2. Frequency calculation for nominal data types3. Norminalize data 4. Aggregation process with relevant attribute to extract useful knowledegs5. Classifying components based on rules from expert using Rule-engine and Opitz codes6. CLassifying components by learning and predicting modules (Decision tree and K-means)7. Compare, evaluate the result between rule-based and learning approaches8. short-out useful information to visualize D ATA VISUALIZATION AND SAVING1. Extract and Saving useful output data to local reposition2. VIsualise output by graphs or charts,...2. Visualise output by interactive Dashboard function (by the previous graph/ charts...) for planner to understand the meaning ofoutput3. Interpretate and saving result CROSS-VALIDATE DIMENSIONAL PREDICTION ALGORITHM GEOMETRY COMPLEXITY PREDICTION ALGORITHM MATERIAL HARDNESS PREDICTION DIMENSIONAL PREDICTION ALGORITHMS Replace this node by your favouritemodel learner Replace this node by theappropriate predictor Replace the node by thenode that produces yourdesired score variable Choose the variableto optimizeDo the final filtering hereConfigure your setof columns hereINPUTFILTER MISSINGLEARNERPREDICTOREVALUATINGLEARNERPREDICTOREVALUATINGNode 131Node 132Node 133COLUMNMANIPULATEMISSING VALUEMATERIAL CODE MATERIAL DATASHEETREMOVEIRRELEVANTDATA TO PROCESSTARGET COLUMNNR to STRNode 170MATERIAL COMPOSITION %Labeling for MaterialLabeling for ComplexityNode 174Node 176Node 179MATERIALDBNode 185Node 186Node 187Node 188Material PredictionNode 190Node 191Node 192Node 193Node 194Node 195Node 196Node 197Node 198Node 199Node 200Node 201Node 202Node 203Node 2081 material codemany designationNode 210Node 211Node 214Node 215Node 217Node 218Node 219RANDOMFOREST_PREDICTGEOMETRYDataToProcess_v2Node 222DATA QualityNode 224Node 225Node 226DATA QualityNode 228Node 229Material encodingNode 234Node 235Node 236Node 237Node 238Node 239Node 240Node 241Node 242Node 243Node 254Features vsCohen kappa Node 270DATA QualityNode 272Node 273LEARNERPREDICTORNode 280Node 281EVALUATINGDo the final filtering hereNode 287Node 289Node 290EVALUATINGPREDICTORDo the final filtering hereNode 295LEARNERPREDICTOREVALUATINGDo the final filtering hereNode 306Node 307Node 308EVALUATINGLEARNERNode 312Node 313Node 314Node 315Node 316Node 317Node 318Balance of dataNode 320ENCODINGData distributionNode 323Node 324Choose the variableto optimizeDo the final filtering hereConfigure your setof columns hereNode 330Node 331 Feature SelectionLoop End Feature SelectionFilter Scorer (deprecated) Feature SelectionLoop Start (1:1) Excel Reader Missing ValueColumn Filter LogisticRegression Learner Logistic RegressionPredictor Scorer Naive Bayes Learner Naive BayesPredictor Scorer DecisionTree Learner Decision TreePredictor Scorer One to Many Missing Value Value Counter Table Creator Column Filter GroupBy Column manipulate Number To String Table Rowto Variable Table Creator Rule Engine Rule Engine Rule-basedRow Filter Partitioning Normalizer Missing Value DecisionTree Learner Decision TreePredictor Scorer Decision TreePredictor Excel Writer Rule-basedRow Filter Normalizer Partitioning Decision TreePredictor DecisionTree Learner Scorer Decision TreePredictor Denormalizer Excel Writer LogisticRegression Learner Logistic RegressionPredictor Denormalizer Logistic RegressionPredictor Excel Writer Joiner GroupBy Scorer Row Filter Random ForestLearner Random ForestPredictor Scorer Random ForestPredictor Denormalizer Excel Writer Excel Writer One to Many Statistics ROC Curve (local) Scorer Scorer Statistics Random ForestLearner Random ForestPredictor One to Many Numeric Outliers ROC Curve (local) ROC Curve X-Partitioner X-Aggregator Scorer X-Aggregator X-Partitioner X-Partitioner X-Aggregator remove dot Box Plot (local) Partitioning Bar Chart FeatureSelection NBC Bootstrap Sampling Statistics Partitioning ROC Curve RFC optimal N_tree RFC optimalmin node size OUTLIER CHART Naive Bayes Learner Naive BayesPredictor X-Partitioner X-Aggregator Scorer Feature SelectionFilter Partitioning Featureselection RFC Random ForestLearner Random ForestPredictor Scorer Logistic RegressionPredictor Feature SelectionFilter Partitioning LogisticRegression Learner Logistic RegressionPredictor Scorer Feature SelectionFilter Partitioning DecisionTree Learner Decision TreePredictor Scorer FeatureSelection DT LogisticRegression Learner Line Plot Table Editor Table Editor Joiner Table Editor Joiner Linear Correlation Value Counter ConditionalBox Plot One to Many Scatter Plot RProp MLP Learner MultiLayerPerceptronPredictor Feature SelectionLoop End Feature SelectionFilter Partitioning Scorer (deprecated) Feature SelectionLoop Start (1:1) String To Number Math Formula(Multi Column) DATA PREPARATION AND ACCESS1. Read data from local storage2. Missing value treatment3. Normalize data4. Re-format error data by format transformation nodes 5. Overview by statistic calculation4. Filter out irrelevant attributes5. Find the method to assign Opitz code position for next analysis step DATA ANALYSIS AND MINING1. Calculate min/ max/ mean values from numerical data2. Frequency calculation for nominal data types3. Norminalize data 4. Aggregation process with relevant attribute to extract useful knowledegs5. Classifying components based on rules from expert using Rule-engine and Opitz codes6. CLassifying components by learning and predicting modules (Decision tree and K-means)7. Compare, evaluate the result between rule-based and learning approaches8. short-out useful information to visualize D ATA VISUALIZATION AND SAVING1. Extract and Saving useful output data to local reposition2. VIsualise output by graphs or charts,...2. Visualise output by interactive Dashboard function (by the previous graph/ charts...) for planner to understand the meaning ofoutput3. Interpretate and saving result CROSS-VALIDATE DIMENSIONAL PREDICTION ALGORITHM GEOMETRY COMPLEXITY PREDICTION ALGORITHM MATERIAL HARDNESS PREDICTION DIMENSIONAL PREDICTION ALGORITHMS Replace this node by your favouritemodel learner Replace this node by theappropriate predictor Replace the node by thenode that produces yourdesired score variable Choose the variableto optimizeDo the final filtering hereConfigure your setof columns hereINPUTFILTER MISSINGLEARNERPREDICTOREVALUATINGLEARNERPREDICTOREVALUATINGNode 131Node 132Node 133COLUMNMANIPULATEMISSING VALUEMATERIAL CODE MATERIAL DATASHEETREMOVEIRRELEVANTDATA TO PROCESSTARGET COLUMNNR to STRNode 170MATERIAL COMPOSITION %Labeling for MaterialLabeling for ComplexityNode 174Node 176Node 179MATERIALDBNode 185Node 186Node 187Node 188Material PredictionNode 190Node 191Node 192Node 193Node 194Node 195Node 196Node 197Node 198Node 199Node 200Node 201Node 202Node 203Node 2081 material codemany designationNode 210Node 211Node 214Node 215Node 217Node 218Node 219RANDOMFOREST_PREDICTGEOMETRYDataToProcess_v2Node 222DATA QualityNode 224Node 225Node 226DATA QualityNode 228Node 229Material encodingNode 234Node 235Node 236Node 237Node 238Node 239Node 240Node 241Node 242Node 243Node 254Features vsCohen kappa Node 270DATA QualityNode 272Node 273LEARNERPREDICTORNode 280Node 281EVALUATINGDo the final filtering hereNode 287Node 289Node 290EVALUATINGPREDICTORDo the final filtering hereNode 295LEARNERPREDICTOREVALUATINGDo the final filtering hereNode 306Node 307Node 308EVALUATINGLEARNERNode 312Node 313Node 314Node 315Node 316Node 317Node 318Balance of dataNode 320ENCODINGData distributionNode 323Node 324Choose the variableto optimizeDo the final filtering hereConfigure your setof columns hereNode 330Node 331 Feature SelectionLoop End Feature SelectionFilter Scorer (deprecated) Feature SelectionLoop Start (1:1) Excel Reader Missing ValueColumn Filter LogisticRegression Learner Logistic RegressionPredictor Scorer Naive Bayes Learner Naive BayesPredictor Scorer DecisionTree Learner Decision TreePredictor Scorer One to Many Missing Value Value Counter Table Creator Column Filter GroupBy Column manipulate Number To String Table Rowto Variable Table Creator Rule Engine Rule Engine Rule-basedRow Filter Partitioning Normalizer Missing Value DecisionTree Learner Decision TreePredictor Scorer Decision TreePredictor Excel Writer Rule-basedRow Filter Normalizer Partitioning Decision TreePredictor DecisionTree Learner Scorer Decision TreePredictor Denormalizer Excel Writer LogisticRegression Learner Logistic RegressionPredictor Denormalizer Logistic RegressionPredictor Excel Writer Joiner GroupBy Scorer Row Filter Random ForestLearner Random ForestPredictor Scorer Random ForestPredictor Denormalizer Excel Writer Excel Writer One to Many Statistics ROC Curve (local) Scorer Scorer Statistics Random ForestLearner Random ForestPredictor One to Many Numeric Outliers ROC Curve (local) ROC Curve X-Partitioner X-Aggregator Scorer X-Aggregator X-Partitioner X-Partitioner X-Aggregator remove dot Box Plot (local) Partitioning Bar Chart FeatureSelection NBC Bootstrap Sampling Statistics Partitioning ROC Curve RFC optimal N_tree RFC optimalmin node size OUTLIER CHART Naive Bayes Learner Naive BayesPredictor X-Partitioner X-Aggregator Scorer Feature SelectionFilter Partitioning Featureselection RFC Random ForestLearner Random ForestPredictor Scorer Logistic RegressionPredictor Feature SelectionFilter Partitioning LogisticRegression Learner Logistic RegressionPredictor Scorer Feature SelectionFilter Partitioning DecisionTree Learner Decision TreePredictor Scorer FeatureSelection DT LogisticRegression Learner Line Plot Table Editor Table Editor Joiner Table Editor Joiner Linear Correlation Value Counter ConditionalBox Plot One to Many Scatter Plot RProp MLP Learner MultiLayerPerceptronPredictor Feature SelectionLoop End Feature SelectionFilter Partitioning Scorer (deprecated) Feature SelectionLoop Start (1:1) String To Number Math Formula(Multi Column)

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: maiyen
Created at: 2021-07-20
On NodePit since: 2021-09-28
Last update: 2024-04-27
Created with KNIME version: v4.4.1

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!