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IoTDataMiningFramework

Usage

  1. Generate IoTDataMiningFramework.jar file.
  2. Deploy and import IoTDataMiningFramework.jar file in eclipse project.
  3. Make sure you have the right pom file with dependencies.
  4. Extend the existing models to create specialized ones

API

Interface DataResource

Package: Framework.resource
Modifier and Type Method and Description
void closeFile()
void getLine(String filename, String delimiter, int num)
void openFile(String filename)
void readAll(String filename, String delimiter)
ArrayList of String readData(ArrayList of DataPoint data, String delimiter, int[] columns, int lblCol, DataPointType type)
ArrayList of DataPoint readData(String delimiter, int column, DataPointType type)
void readSome(String filename, String delimiter, int limit)

Abstract Class DataPoint

Package: Framework.data.DataPoint
Modifier and Type Method and Description
abstract Label getLabel() Assigned label of training data.
String getName()
int getSequenceID()
java.sql.Timestamp getTimestamp()
DataPointType getType()
java.lang.Object getValue()
abstract java.lang.Object getValue(java.lang.String column) Get sample data value from specified column.
boolean has(Feature feature)
void setName(java.lang.String name)
void setSequenceID(int seq)
void setTimestamp(java.sql.Timestamp timestamp)
void setType(DataPointType type)

Interface TimeSeries

Package: Framework.algorithm.Timeseries
Modifier and Type Method and Description
double addDataPoint(T dp)
void calculateStdDev(T dp)
double getAverage()
double getStdDev()
void reset()

Abstract Class DecisionTree

Package: Framework.algorithm.classification.decisiontree.DecisionTree
Modifier and Type Method and Description
Label classify(DataPoint dataSample) Classify dataSample.
Node getRoot() Get root.
void printSubtree(Node node)
void printTree()
void train(List of DataPoint trainingData, List of Feature features) Trains tree on training data for provided

Interface ImpurityCalculationMethod

Package: Framework.algorithm.classification.decisiontree.impurity
Modifier and Type Method and Description
double calculateImpurity(List of DataPoint splitData) Calculates impurity value.
default double getEmpiricalProbability(List of DataPoint splitData, Label positive, Label negative) Calculate and return empirical probability of positive class.

Abstract Class Label

Package: Framework.algorithm.classification.decisiontree.label
Modifier and Type Method and Description
abstract boolean equals(Object o) Force overriding equals.
abstract String getName()
abstract String getPrintValue() Label value used to print to predictions output.
abstract int hashCode() Force overriding hashCode.