AnomalyDetection

This Project aim of implements most of Anomaly Detection Algorithms in Java. If you want to contribute source code, please write Email to [email protected], or you can add my WeChat Number: JeemyJohn

1. Isolation Forest

This algorithm is realized in package iforest.
Algorithm Home Page: http://blog.csdn.net/u013709270/article/details/73436588

Source Paper:
             http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf
             http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/tkdd11.pdf

Usage step:

1. Create an object of class IForest

      IForest iForest = new IForest();

2. Get samples and train

      double[][] samples = new double[1000][2];
      
      //get samples
      ...
      
      int[] ans = iForest.train(samples, 100);

     We have two declaration of function train, the implementation of them are same to each other. The only difference of them is one have default parameter. As the results of function train ans, if ans[i]==0 means it's an Anomaly(or Isolation) Point, else a Normal Point.

3. Predict a new sample

     If a sample does not in samples, we can use function predict to judge it a Normal point or not.

     double[] sample = ...
     int label = iForest.predict(sample);
     System.out.println(label);