# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 4 Identified Data Mining Methods  
Data stored in blockchain can definitely be considered to be Big Data [30; 31]. For example, the full blockchain size of the most popular cryptocurrency Bitcoin is more than 300 gygabytes at the time of writing this paper ([[https://www.statista.com/statistics/647523/worldwide-bitcoin-blockchain-size/](https://www.statista.com/statistics/647523/worldwide-bitcoin-blockchain-size/)]([https://www.statista.com/statistics/647523/worldwide-bitcoin-blockchain-size/](https://www.statista.com/statistics/647523/worldwide-bitcoin-blockchain-size/)) (accessed on 15 August 2021)). Very sophisticated Data Mining tools and methods are usually used or developed for deep analysis of these data. There exists a wide pool of Data Mining methods nowadays, such as, for example, from Regression Analysis to more complex ML methods, Random Forest or Support Vector Machines [32]. Typically, these methods are used to cope with Association Rule Mining, Numerical Association Rule Mining, clustering, classification and others. Data Mining can also be viewed as a part of the KDD (Knowledge Discovery in Databases) [33] process, where the term KDD refers to the overall process of discovering useful knowledge from various data [33]. In this process, DM is actually an application of algorithms aimed to extract patterns from data. In line with this, KDD is a very complex process which consists of six phases [34; 35]:  
* Problem/application specification (a problem is introduced which plays the main role)
* Problem/application understanding (problem tries to become explainable),
* Data preprocessing, (data cleaning, data transformation, feature selection procedures take place)
* Data mining, (an actual model is built on preprocessed data and knowledge is extracted)
* Evaluation, (interpretation of results takes place)
* Result exploitation (visualisation of discovered knowledge, generation of reports, narrating stories). Many research papers and practice have revealed that data preprocessing is probably the hardest step in the overall process of the KDD. If data are well prepared, cleaned and transformed, then the subsequent processes that follow occur more smoothly. Loosely speaking, the data preprocessing step, in most literature, is considered to be the most significant and most time consuming process in the whole pipeline [34]. Blockchain data are also very interesting from this perspective, because they usually involve a couple of additional steps that are not necessary for the data stored in spreadsheets, transaction databases and similar sequential-based formats. Blockchain data are, typically, stored in specially prescribed formats, which ensures data immutability. Data are structured into sets of valid transactions, which are packed into blocks. A block of transactions holds a reference in the form of a hash value to the content of the previous block. Each block is sealed cryptographically and appended to the end of the ledger. Therefore, retrieving data from a blockchain is not easy or effortless. It requires, firstly, to use specific parsersfor each blockchain, in order to extract raw data and make a systematic extraction and presentation of these data. Some examples of parsers can be found in the following links ([[https://github.com/alecalve/python-bitcoin-blockchain-parser](https://github.com/alecalve/python-bitcoin-blockchain-parser)]([https://github.com/alecalve/python-bitcoin-blockchain-parser](https://github.com/alecalve/python-bitcoin-blockchain-parser)) (accessed on 15 August 2021)) ([[https://github.com/gcarq/rusty-blockparser](https://github.com/gcarq/rusty-blockparser)]([https://github.com/gcarq/rusty-blockparser](https://github.com/gcarq/rusty-blockparser)) (accessed on 15 August 2021)). The following heatmap (Figure 3) presents all Data mining methods that were identified in our study.