<![CDATA[This study implements the Hierarchical Clustering algorithm with Ward linkage and Euclidean distance methods to analyze 26 crypto narratives based on the Fully Diluted Market Cap (FDMC) metric. Using a hybrid method that integrates Waterfall, Cross-Industry Standard Process for Data Mining (CRISP-DM), and Knowledge Discovery in Databases (KDD), data was obtained from the CoinGecko API, manually clustered, and aggregated per narrative. Pre-processing involved logarithmic transformation (log-10) and Z-Score normalization to address power-law distributions and outliers, resulting in a more stable cluster structure. The clustering results mapped the market into five clusters: Bluechip (L1 with FDMC $2.76T), Growth (PAY, MEME, CEX, DEX, DeFi totaling $468.22B), Growth (AI, DePIN, DAO, L2, RWA, ORC, GameFi, XCH, DID, PRC, LST with $192.91B), Speculative (NFT, MET, SocialFi, BTC Eco, W3I with $17.55B), and Speculative (LPD, GambleFi, FTO, SEC with $2.34B). The model was validated with a Silhouette Score of 0.650 and a Cophenetic Correlation Coefficient of 0.647, indicating cohesive and representative clusters. A web-based implementation using Django, D3.js, and Chart.js provides interactive visualizations and portfolio recommendations. Contributions include a novel fundamental valuation approach, an adaptive clustering model, and practical analytical tools for investors, with potential expansion to multidimensional metrics in the future.]]>
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