  The Non-Fungible-Token (NFT) market has experienced explosive growth in
recent years. According to DappRadar, the total transaction volume on OpenSea,
the largest NFT marketplace, reached 34.7 billion dollars in February 2023.
However, the NFT market is mostly unregulated and there are significant
concerns about money laundering, fraud and wash trading. The lack of
industry-wide regulations, and the fact that amateur traders and retail
investors comprise a significant fraction of the NFT market, make this market
particularly vulnerable to fraudulent activities. Therefore it is essential to
investigate and highlight the relevant risks involved in NFT trading. In this
paper, we attempted to uncover common fraudulent behaviors such as wash trading
that could mislead other traders. Using market data, we designed quantitative
features from the network, monetary, and temporal perspectives that were fed
into K-means clustering unsupervised learning algorithm to sort traders into
groups. Lastly, we discussed the clustering results' significance and how
regulations can reduce undesired behaviors. Our work can potentially help
regulators narrow down their search space for bad actors in the market as well
as provide insights for amateur traders to protect themselves from unforeseen
frauds.
