$ python list_field.py v2_papers/ analysis_method.algorithms.name K-means;8 Naive Bayes;7 naive_bayes;4 Support Vector Machine;4 Random Forest;4 Nearest Neighbor;4 Decision Tree;4 decision_tree;3 SVM;3 K-Means;2 fuzzy clustering;2 Fuzziness based divide-and-conquer;2 Gaussian Mixture Models;2 C4.5;2 k-means;2 Autoencoder;2 Wavelet Analysis;2 mad-based outlier removal;2 support vector machine;2 k nearest neighbors;2 Entropy-based Traffic Anomaly Detection;1 clustering by hand in plotted graphs;1 SNORT;1 Ambiguity detection;1 Traffic Activity Graph;1 Naïve Bayes;1 Random forest;1 Softmax Regression Classifier;1 RIPPER;1 neural_network;1 ARMA;1 a-plane clustering;1 suricata;1 Autoregressive model;1 graph matching;1 clustering consensus;1 Logistic Regression;1 mlp;1 Decision tree;1 multi-class support vector machine;1 Neural Nets;1 Latent Semantic Analysis;1 Statistical Fingerprinting;1 hierarchical agglomerative;1 multiway subspace method;1 svm;1 viterbi;1 hidden markov model;1 two step clustering;1 AdaBoost;1 Hidden Markov Model;1 Principal Component Analysis;1 Holt-Winters Forecasting;1 Entropy;1 k-Nearest-Neighbors;1 Growing Hierarchical Self-Organizing Map;1 Ensemble;1 Bayesian Network;1 Bayes Net;1 Naı̈ve Bayes with kernel density estimation;1 Minimum Spanning Tree Clustering;1 k-medoids;1 pcStream2;1 Adaboost with Decision Stumps;1 Restricted Boltzmann Machine;1 HMM;1 Classification validation;1 Maximum entropy;1 Naı̈ve Bayes with kernel density estimation and FCBF prefiltering;1 neural network;1 vector quantization;1 bayes_network;1 Expectation Maximization;1 CS4.5;1 cross-plane correlation;1 ARIMA;1 Isolation Forests;1 Threshold based rule;1 KNN;1 neurofuzzy;1 Statistical Analysis;1 Naïve Bayes with FCBF prefiltering;1