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March 25, 2026
Wallet Finder
February 18, 2026
Clustering in DeFi is all about grouping blockchain wallets and transactions based on behavior. It organizes messy blockchain data into patterns that help traders and analysts spot trends, detect risks, and even mimic successful strategies. By analyzing things like transaction timing, amounts, and smart contract usage, clustering can reveal hidden connections between wallets, uncover coordinated trading strategies, and expose suspicious activities like wash trading or bot networks.
Key Takeaways:
Tools like Wallet Finder.ai make this process user-friendly by offering real-time alerts, performance tracking, and comparisons to help traders improve their strategies. While challenges like pseudonymity and cross-chain activity remain, advancements in machine learning and real-time processing are making clustering more effective every day.
Clustering in the DeFi space involves grouping blockchain data into meaningful categories, helping to uncover patterns and relationships between wallets and transactions. Various methods are used to achieve this, each offering a unique lens for interpreting data. When combined, they create a powerful toolkit for understanding DeFi activities.
Hierarchical clustering organizes wallets into a tree-like structure based on behavioral similarities. It starts by analyzing individual wallets and gradually groups them by shared traits. This method can identify broad categories and then break them down into smaller, more specific clusters.
For instance, hierarchical clustering might group high-frequency traders together, then further divide them into sub-groups like arbitrage bots, market makers, and day traders. Each sub-group shares specific behaviors while still fitting into the broader category.
What makes this approach stand out is its ability to show nested relationships. For example, a wallet could belong to a cluster of profitable traders, which is part of a larger group of active DeFi users, and ultimately within an even broader category of sophisticated investors. This layered view helps traders see not only what successful wallets are doing but also how they fit into the bigger picture of the market.
That said, hierarchical clustering can be resource-intensive, especially with large datasets. It also requires careful adjustments, like choosing the right number of clusters and selecting distance metrics, to ensure accurate results. Understanding the risks of misleading information is equally important. Our discussion on Telegram Signal Groups vs. Paid Scams: Key Differences highlights how to spot reliable sources and avoid costly mistakes in the crypto space.
Heuristic methods rely on observable blockchain behaviors to connect addresses or uncover trading strategies. These rules are straightforward and quick to apply, making them a popular choice for clustering.
One commonly used heuristic is common input ownership. When several addresses contribute to the same transaction, it often signals that they belong to the same entity. For example, a trader might pool funds from multiple addresses to execute a single large swap.
Another useful rule is change address analysis. When someone sends a transaction, leftover funds often go to a new "change" address. By tracking these patterns, analysts can link addresses to the same wallet owner.
Gas price patterns also offer insights. Wallets controlled by the same entity often exhibit consistent gas strategies - some always pay standard prices, others overpay for speed, while some use advanced gas-saving tools. These patterns act like fingerprints, making it easier to group related addresses.
Transaction timing is another clue. Wallets managed by the same trader often show similar activity schedules, like being active at certain hours or reacting to market events in sync. Automated trading bots are particularly easy to spot because of their precise timing.
The main advantage of heuristic methods is their speed and clarity. They work well with large datasets and produce understandable results. However, they can sometimes misidentify unrelated users who coincidentally behave similarly, or they might be tricked by users employing advanced privacy techniques.
Smart contract creation analysis looks at how developers and organizations deploy contracts, providing another layer of insight into DeFi activity. This method tracks the relationships between contract creators and the protocols they build, revealing patterns in how DeFi infrastructure evolves.
When a smart contract is deployed, it permanently links the creator to the protocol. By analyzing these deployments, clustering algorithms can identify developers, institutional players, and advanced users who create their own tools for trading or interacting with DeFi platforms.
This approach is especially useful for monitoring the growth of DeFi protocols. Many projects start with a single developer deploying contracts, then grow to include team members, external contributors, and an ecosystem of related contracts. Tracking these patterns sheds light on the different stages of protocol development.
Interaction with new contracts also provides valuable clues. Wallets that frequently interact with newly deployed contracts are often early adopters, beta testers, or developers. These users tend to have higher-than-average returns because they access opportunities before the wider market catches on. Tools like Wallet Finder.ai use these patterns to track protocol development and spot early opportunities.
However, this method has its challenges. Proxy contracts and upgradeable protocols can complicate the link between creators and the final implementation. Additionally, factory patterns or intermediary systems used by organizations can obscure the true relationships.
Each of these clustering methods offers unique strengths. Hierarchical clustering gives a big-picture structure, heuristic methods uncover specific connections, and smart contract analysis adds context about protocol development. When used together, they provide a detailed and comprehensive view of DeFi participants and their behaviors, offering valuable insights into the ever-evolving blockchain ecosystem.
The article presents clustering methods as powerful analytical tools without quantifying how accurate they are in practice. Published academic research on DeFi wallet clustering provides specific precision and recall figures that significantly change how much confidence an analyst should place in clustering outputs, and which types of clustering errors are most common.
Research on common input ownership heuristics applied to Ethereum data, including studies published through the IEEE Symposium on Security and Privacy and academic preprints on the Social Science Research Network, consistently shows precision rates between 70% and 85% for correctly linking addresses that genuinely belong to the same entity. The recall rates, meaning the percentage of same-entity address pairs that the heuristic successfully identifies, are lower: typically 40% to 60%. The implication is that common input ownership clustering produces a meaningfully conservative estimate of same-entity address sets. It identifies many true connections but misses roughly half of all genuine connections, because many transactions from multi-wallet entities are structured in ways that do not trigger the common input criteria.
The false positive rate, meaning incorrectly linking two addresses that belong to different entities, is most commonly caused by CoinJoin transactions and coordinated DeFi protocol interactions where multiple independent users participate in the same transaction simultaneously. Uniswap v2's liquidity provision mechanism, for example, sometimes groups independent users' transactions in ways that create apparent common input signals between wallets that have no actual relationship. Analysts applying common input ownership clustering to DeFi data without filtering for these protocol-specific transaction types produce inflated cluster sizes and incorrectly attribute coordinated institutional behaviour to what is actually independent retail activity.
Clustering algorithms allow analysts to set thresholds that determine how much behavioural similarity is required before two addresses are grouped. Conservative thresholds require multiple independent heuristic signals to align before linking addresses, producing smaller clusters with higher precision but missing more genuine connections. Aggressive thresholds link addresses on weaker evidence, producing larger clusters that catch more genuine connections but include more false linkages.
The choice between these settings has direct consequences for trading applications. An analyst using conservative clustering to identify a whale's complete wallet set will miss a portion of that whale's actual addresses, potentially underestimating their position size and misreading the direction of their net activity. An analyst using aggressive clustering may attribute trades from unrelated parties to the same entity, manufacturing a pattern of coordinated activity that does not exist. Neither error is preferable, but they have different downstream effects. For trading applications where missing genuine connections causes missed signals, moderately aggressive thresholds are preferable. For compliance and fraud detection applications where false positives cause operational harm, conservative thresholds are the appropriate choice.
By grouping wallets into clusters, we can uncover patterns that shed light on market behavior, trading strategies, and potential opportunities within the DeFi ecosystem.
Studies show that clustering highlights five main transaction flow types: deposit, withdrawal, token swaps, liquidity provision, and minting/burning cycles. These flow types help to map out how various wallet behaviors take shape and interact within the DeFi space.
Clustering analysis also reveals distinct wallet behavior profiles. For instance, liquidity providers tend to form clusters characterized by fewer but high-value transactions. On the other hand, early adopters create separate clusters by engaging with new protocols well before the broader market catches on.
Clustering doesn’t just highlight common behaviors - it also uncovers unconventional trading strategies. These include cyclic arbitrage, MEV activities (such as front-running, back-running, and sandwich attacks), and flash loan schemes - strategies often overlooked by traditional analysis methods.
"Our work extends this body of research by proposing a novel motif extraction technique to infer DeFi methods. These motifs serve as building blocks for analyzing more complex interactions, including arbitrage and MEV transactions. For companies operating in the DeFi space, this approach offers a robust method for monitoring account activities and detecting trading strategies and anomaly behaviors."
The article mentions MEV activities including front-running, back-running, and sandwich attacks as patterns that clustering uncovers. The full MEV taxonomy is more detailed than this, and each MEV type creates a structurally distinct on-chain pattern that clustering identifies through different signatures. Understanding the complete taxonomy matters for analysts trying to distinguish MEV activity from genuine trading, and for traders trying to assess whether their own transactions are being exploited.
Sandwich attacks are the most commonly discussed MEV type and the most visible to retail traders. The signature is a three-transaction sequence: a buy transaction from a bot wallet immediately before a large victim transaction, the victim transaction itself, and a sell transaction from the same bot wallet immediately after. The clustering fingerprint is a bot address that appears as both buyer and seller in consecutive blocks across many different tokens, with the buy and sell always bracketing a third-party transaction. Research by Flashbots estimated sandwich attack extraction at approximately $1.3 billion annually across Ethereum at peak in 2022.
Pure arbitrage creates a different signature: a bot wallet that buys a token on one DEX and sells it on another within the same transaction, capturing a price discrepancy. Unlike sandwich attacks, pure arbitrage creates no direct harm to other users and in fact benefits the market by correcting price inefficiencies across platforms. The clustering pattern is a wallet that consistently executes multi-hop swap paths touching two or more DEXes in a single transaction, with no holding period between buy and sell.
Liquidation MEV occurs when a bot monitors DeFi lending protocols and executes liquidations the moment a borrower's collateral ratio drops below the liquidation threshold. The clustering signature is a wallet that makes no ordinary trades but consistently appears as the "liquidator" address in protocol liquidation events across Aave, Compound, and similar platforms. These wallets often have large ETH or stablecoin balances maintained specifically as liquidation capital. Research from EigenPhi estimated liquidation MEV at approximately $800 million annually across major Ethereum lending protocols.
Just-in-Time (JIT) liquidity is less discussed but increasingly significant. A JIT bot adds liquidity to a Uniswap v3 pool in the same block as a large swap, captures the fees from that swap due to their concentrated liquidity position, and removes their liquidity in the block immediately following. The clustering signature is a wallet that makes paired add-liquidity and remove-liquidity transactions separated by exactly one block, consistently timed around large swap events. The victim is the regular LP whose fee share is diluted by the JIT provider's perfectly timed deposit.
Time-bandit attacks are the most structurally severe MEV type and the most theoretically dangerous to blockchain security. A time-bandit attack occurs when the MEV available in historical blocks is large enough to incentivise a miner or validator to reorganise the blockchain and reprocess past blocks to capture those MEV opportunities. This attack has not been widely executed on Ethereum post-merge because the proof-of-stake mechanism significantly increases its difficulty, but it remains a theoretical risk. The clustering signature would be unusual validator behaviour patterns in block proposal timing, which is monitored by MEV-aware blockchain security researchers rather than standard wallet analysts.
The five types together represent the following rough distribution of MEV extraction: sandwich attacks capture the largest share of retail-harming MEV, pure arbitrage is the largest by total volume but distributes benefits across the market, liquidation MEV is concentrated among a small number of professional bot operators, JIT liquidity is growing as Uniswap v3 adoption increases, and time-bandit attacks remain theoretical in current market conditions.
Traders who use clustering insights can uncover profitable opportunities and replicate successful strategies. By building on these insights, they can make smarter, more informed trading decisions.
Wallet Finder.ai makes it easy to track wallets with strong profit histories by offering clear performance metrics. You can apply filters for factors like profitability, win streaks, and consistency to identify wallets that are early movers - often before they attract widespread attention.
To stay ahead, real-time alerts through Telegram notify you of significant activity from monitored wallets. This is especially useful when tracking wallets that trade quickly during sudden market changes. You can create watchlists of high-performing wallets and get instant updates when these wallets make major trades or take new positions.
After identifying top wallets, you can narrow your focus further using advanced filtering options.
Each cluster of wallets behaves differently, and Wallet Finder.ai’s advanced filters let you zero in on the ones that matter most to your trading style. For instance, you can focus on liquidity providers who make fewer but larger trades, or active traders who thrive on short-term price swings.
Exporting blockchain data allows you to compare how different clusters perform under various market conditions. For example, some clusters might excel during volatile periods, while others perform better in stable markets. These insights help you fine-tune your strategy to align with the current market environment.
When studying trading strategies, you can analyze key factors like trade timing, position sizes, and risk management tactics. For instance, high-performing liquidity provider clusters might enter trades during specific conditions or use portfolio allocations that reduce impermanent loss. These observations provide a blueprint for refining your own approach.
Beyond market trends, comparing your personal performance to these clusters can reveal deeper strategic insights.
By linking your personal wallet, you can measure your trading performance against profitable clusters. This comparison highlights gaps in your strategy and helps you make adjustments to improve your returns.
Wallet Finder.ai also tracks your performance over time, showing how your strategies evolve and their impact. You can see which cluster your trading behavior most closely resembles and use that knowledge to refine your approach.
For example, if you’re mainly a liquidity provider, you can compare your returns to other wallets in the same cluster to gauge whether you're optimizing your strategy. Similarly, if you’re an active trader, you can measure your success rate against other active trading wallets.
These clustering-based tools empower traders with actionable insights, ensuring Wallet Finder.ai remains a valuable resource for real-time decision-making.
Clustering analysis helps uncover patterns in DeFi trading, but current methods face obstacles that limit their accuracy and efficiency. These challenges are driving efforts to refine and innovate clustering techniques.
Pseudonymity complicates identity tracking across wallet addresses. Traders often use multiple wallets, making it difficult for clustering algorithms to link related activities correctly. This can lead to fragmented or inaccurate classifications.
Shifting transaction behaviors disrupt established clusters. As market conditions change, wallets may adopt new strategies. For example, a wallet previously identified as a "conservative liquidity provider" might pivot to aggressive arbitrage, rendering earlier classifications unreliable.
Scalability is a hurdle when processing massive blockchain datasets. Analyzing millions of transactions requires significant computational power, which becomes especially challenging during high-frequency trading or network congestion.
Cross-chain activity adds complexity. Traders operating across networks like Ethereum, Polygon, and Arbitrum can remain partially hidden if clustering is restricted to a single blockchain. This limitation obscures the full scope of their trading strategies.
Smart contract interactions introduce noise. Automated protocols, MEV bots, and contract-to-contract transactions can clutter data, making it harder to isolate genuine trading patterns. Addressing this noise is essential for improving clustering accuracy.
Advanced machine learning models offer potential solutions to these challenges. Neural networks trained on complex transaction data could detect subtle patterns that traditional methods overlook. These models might also identify when address changes represent a single trader, improving the reliability of clusters.
Dynamic clustering techniques could adapt to evolving market conditions. Unlike static groupings, these systems would continuously update clusters based on recent activity while preserving historical context. This adaptability would support real-time insights on platforms like Wallet Finder.ai.
Cross-chain clustering integration is a key area for growth. By aggregating data across multiple blockchains, future systems could create comprehensive profiles of traders operating across networks. This approach would reveal strategies that span different chains, offering a clearer view of sophisticated trading behaviors.
Privacy-preserving methods could enhance clustering accuracy without compromising anonymity. Techniques like zero-knowledge proofs may allow for pattern detection while safeguarding sensitive transaction details.
Real-time processing capabilities will become increasingly critical as DeFi markets accelerate. Clustering systems need to analyze transactions within seconds to provide immediate alerts and insights, keeping pace with fast-moving markets.
Combining on-chain and off-chain data could further refine clustering analysis. Integrating blockchain data with external factors like social media sentiment, news trends, and traditional market indicators could generate more robust trading signals and classifications.
The future of clustering technology in DeFi will likely focus on balancing precision with privacy, speed with depth, and automation with human oversight. Platforms that successfully adopt these advancements will offer traders powerful tools to navigate the intricate and rapidly evolving DeFi landscape.
The article identifies cross-chain activity as a clustering challenge and mentions cross-chain integration as a future improvement direction. The current state of cross-chain clustering deserves a more specific treatment because several platforms have already made meaningful progress, the technical approaches differ substantially, and the accuracy trade-offs between methods are directly relevant to analysts trying to use cross-chain data today rather than waiting for future solutions.
Single-chain clustering benefits from a complete, ordered transaction record where every transaction is visible to every observer and where temporal ordering is guaranteed by block sequence. Cross-chain clustering has none of these properties. Two addresses that belong to the same entity but operate on different chains leave no direct on-chain connection unless they interact through a bridge. Even when they do use a bridge, the bridge transaction connects two addresses without proving that those addresses belong to the same entity, because bridge transactions are available to anyone regardless of wallet ownership history.
The structural problem is that the same person can hold an Ethereum wallet, a Solana wallet, and an Arbitrum wallet with no on-chain connection between them. Creating those connections requires either bridge interaction records (which exist but are sparse), behavioural correlation across chains (which is probabilistic rather than definitive), or off-chain data enrichment from exchanges that have collected KYC information associating multiple chain addresses to a single user (which is available to regulators but not to public analysts).
The following approaches represent the current state of cross-chain clustering, with meaningful differences in how they work and what they can reliably achieve:
Nansen and Arkham Intelligence both implement combinations of the first two approaches, using confirmed bridge interactions as high-confidence anchors and supplementing with behavioural correlation where bridge data is unavailable. Neither platform publishes precision and recall figures for their cross-chain entity attribution, but informed estimates from practitioners who have evaluated their outputs suggest cross-chain linkage accuracy of 60% to 75% precision for the behavioural correlation component, with the bridge-interaction component achieving near-100% precision for the addresses it covers. The implication for analysts is that cross-chain entity attribution from current platforms is useful as a directional signal but should not be treated as definitive without additional corroborating evidence.
Clustering wallet addresses is a powerful way to uncover transaction patterns in DeFi data. By grouping related wallets and analyzing their behaviors, it becomes possible to spot opportunities that might otherwise stay hidden in the pseudonymous world of blockchain.
For investors, clustering can help improve portfolio strategies. By grouping assets with similar behaviors, it’s easier to reduce risks and avoid overlapping investments. On top of that, clustering sheds light on hidden liquidity flows and trading strategies, offering a better understanding of institutional movements and market trends. This insight allows traders to pinpoint key price levels with high activity and refine their entry and exit strategies based on a more complete view of the market.
Clustering also deepens our knowledge of transaction behaviors and wallet activity. As Nansen Intern aptly stated:
"Transaction clustering represents the ongoing balance between blockchain transparency and user privacy." - Nansen Intern
For traders using tools like Wallet Finder.ai, clustering analysis is a game-changer. It lays the groundwork for identifying top-performing wallets, analyzing historical trends, and getting real-time alerts about significant market shifts. Wallet Finder.ai’s real-time notifications and performance tracking are prime examples of how clustering can be applied effectively.
While challenges like pseudonymity, scalability, and cross-chain complexities remain, clustering technology continues to show potential. Advances in machine learning, dynamic clustering methods, and cross-chain integration will further improve accuracy and expand its capabilities. As DeFi markets grow and change, clustering analysis will be a vital tool for navigating this complex space and finding profitable opportunities.
Clustering in DeFi plays a key role in spotting patterns within transaction behaviors. By grouping similar transactions, it becomes easier to identify potential risks, address systemic weaknesses, and uncover suspicious activities. This method also sheds light on opportunities to reduce fraud or other harmful actions.
For traders and platforms alike, clustering offers tools to track transaction trends, set up early warning systems for emerging risks, and strengthen security measures. By studying these patterns, users can make smarter decisions and adjust their strategies to achieve better results within the DeFi space.
Clustering methods in DeFi face some tough hurdles. One big challenge is the risk of grouping wallet addresses incorrectly, especially with advanced privacy tools and the intricate nature of blockchain transactions. On top of that, vulnerabilities in smart contracts and limited access to trustworthy data can make accurate analysis even harder.
To tackle these issues, researchers are working on advanced algorithms that can better understand transaction types and identify entities more effectively. Machine learning is also stepping in to spot anomalies and evaluate risks, helping clustering methods become more precise and reliable as they evolve.
Traders can use clustering patterns in DeFi to spot wallet behaviors and transaction trends that might signal profitable opportunities. By studying how wallets group together and interact, traders can discover chances for arbitrage, recognize large-scale trading moves, and even predict potential market changes.
Techniques like graph-based wallet analysis dig deeper, uncovering connections between related addresses and exposing hidden trading activities. These insights allow traders to fine-tune their strategies, improve the timing of their market entries and exits, and adjust to shifting conditions - leading to better chances of boosting profits.
The practical answer involves applying two validation tests before trusting any cluster-derived signal for trading decisions. The first test is temporal consistency: does the cluster behave as a single entity over time, or do different addresses in the cluster show divergent behaviour patterns after the point where they were linked? A genuine single-entity cluster will show correlated activity throughout the observation period. A false cluster that linked two unrelated entities will show divergent patterns, with each address responding independently to market events rather than acting in coordination.
The second test is transaction frequency consistency: does the total transaction volume across the cluster's addresses add up to a plausible single-entity activity level? An entity that supposedly controls 20 addresses but shows 200 simultaneous independent transactions across those addresses is almost certainly a false cluster grouping unrelated users. A genuine entity controlling 20 addresses would show coordinated rather than independent activity patterns.
Tools that provide confidence scores alongside cluster assignments, rather than binary linked or not-linked outputs, give you the most useful input for this validation. Nansen's entity labelling system includes implicit confidence levels based on the number and type of signals supporting each label. Treating labels supported by a single weak signal differently from labels supported by multiple independent signals is the correct calibration approach.
These are complementary rather than competing approaches, and the distinction matters for understanding what each method can and cannot tell you. Clustering is a bottom-up process that starts from raw transaction data and discovers groupings based on behavioural similarity without prior knowledge of who the wallets belong to. Wallet labelling is a top-down process that assigns known identities to specific addresses based on off-chain information, such as exchange hot wallet addresses identified from public announcements or protocol treasury addresses identified from governance documentation.
Clustering is more powerful for discovering unknown entities and strategies because it can identify coordinated behaviour between wallets that have never been publicly associated. Wallet labelling is more precise for known institutional actors because it provides ground-truth identity rather than probabilistic grouping. The most effective analytical approach uses labelled addresses as anchors that constrain and validate clustering outputs. When a clustering algorithm groups an unknown address with a known exchange hot wallet, the label provides the interpretation context that the cluster alone cannot supply.
For traders specifically, labelling provides the "what" (this is a Binance wallet, this is a known market maker) while clustering provides the "who else" (these five unknown addresses appear to be coordinated with that known entity). Using both together produces the most actionable picture of institutional activity in any given token or protocol.
Wash trading detection through clustering is one of the more mature applications of the technique in DeFi, with documented accuracy rates higher than most other clustering use cases because wash trading produces particularly distinctive patterns that are difficult to disguise.
The characteristic signature of wash trading is a set of addresses that repeatedly send the same asset between each other, generating transaction volume without net change in holding position. In clustering terms, this appears as a strongly connected subgraph where all nodes are in the same cluster and all edges are bidirectional across a small number of tokens and protocols. The ratio of intra-cluster transaction volume to external transaction volume is extremely high for wash trading clusters, often above 90%, compared to 20% to 40% for legitimate coordinated trading operations that also trade externally.
Published research on wash trading detection in DeFi NFT markets, including studies examining Ethereum-based NFT marketplaces, found clustering-based detection methods achieving precision above 90% for identifying wash trading rings once the cycle frequency threshold was set correctly. The main failure mode is "diluted wash trading" where a wash trading operation intersperses genuine external trades with the circular internal trades to reduce the intra-cluster ratio below detection thresholds. This dilution strategy is used by more sophisticated operations and reduces detection precision to approximately 60% to 70% at the diluted activity level, making it less reliable but still significantly better than random.
For DeFi token markets, where wash trading occurs primarily to inflate volume metrics used by aggregator platforms to rank tokens, the detection threshold can be calibrated specifically for the volume inflation use case: addresses that generate more than 50% of a token's daily volume while maintaining zero net change in their token balance are wash trading by definition, regardless of whether they cluster with other identified wash traders.
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