# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 6 Discussion
Data cleaning involves the actions of filtering and excluding data that cannot be used or is irrelevant. Data that are excluded can include irrelevant fields [45; 46; 72; 80; 141; 152], invalid fields [42; 120], inactive accounts [46; 111; 113; 142], duplicate addresses and data [54; 111; 152], outliers [102], and data with unknown labels [40; 80]. Additionally, missing values can be filled with a median value of the column [42; 139] or with certain default values [148]. Peak values can be eliminated by subtracting the column mean and dividing it with the standard deviation [42]. Data can be further normalised [105; 127; 51] and, when working with smart contracts, the operators and operands can be removed from the opcodes [63; 117; 131]. When working with transactions, if needed, the data can be grouped by user or address [42; 77; 111; 153]. If the data set seems to be too small, it can be expanded by bootstrapping the original data [101]. To be able to work with the collected data, they should be labelled [118; 123; 148; 150; 156]. The labelling can either be done manually [48; 78; 120] or automatically [44; 59] by using certain tools, like Oyente [63] or Wappalyzer [144]. Feature extraction can be done by using several tools, like the bi-gram features [49], RFM features [42], the tsfeatures library [46], or the n-gram algorithm [63; 117]. The mutual influence of features can be calculated using [PERSON]'s correlation [42]. The third level is devoted to the selection of the data mining method. The selection of data mining is done mostly by conducting literature reviews and research. Most of the articles included in our study also evaluated multiple methods. The methods were either selected to show the performance of their custom solution, or were evaluated to choose the best method for a specific problem. Figure 4 shows the trends of using various methods over the past five years. In the first years that were considered in this study, the authors utilised mostly conventional machine learning methods, e.g., Random Forests. However, a very interesting trend appeared in recent years, where the use of deep learning methods was in the majority, which is not surprising due to the popularity of deep learning [37]. The taxonomy is concluded with an evaluation of results, as well as a visualisation of the obtained results in level 4. Most of the evaluation results given by the reviewed publication were given by presenting the precision, recall, F-score and accuracy. Other metrics include the processing and training time, the Micro and Macro F-score [53; 63], fall out [93], loss [67; 87; 97; 103; 162], Jaccard, NMI [75], True Positive Rate (TPR) and False Positive Rate (FPR) [45; 66; 91; 100; 121; 123; 135; 144; 147], Kappa value [43], and MCC score [58; 65; 137]. The visualization was done by presenting the ROC curves [40; 51; 63; 67; 81; 93; 94; 103; 121; 125; 140; 141; 156], AUC [49; 58; 66; 93; 112; 113; 138; 140; 152], ROC-AUC [44; 158], the t-SNE algorithm [42; 158], 2D and 3D scatter plots [42], confusion matrix [50; 59; 72; 103; 108; 119; 139; 140; 142; 146; 147; 149; 150; 152; 168], detection frequencies [50], PSI graph and SCG [94], the  
Figure 4: Distribution of used methods per year. Dirichlet distribution [52], Precision Recall Curves [125], Radar graphs and Heatmaps [80], and other suitable graphs.