Explore cryptocurrency market trends with Python using unsupervised learning techniques. Using Jupyter Notebooks to implement K-means clustering and Principal Component Analysis (PCA) to analyze and predict price trends of cryptocurrencies over 24-hour and 7-day periods.

Simple ipynb for testing and demonstrating scikit-learn KMeans clusterization algorithm and finding the Optimal Cluster Number

PubMedから実際のTreg関連文献を収集し、Enhanced Treg Vocabulary（7層316用語）を用いて高精度に分類。品質評価指標（Silhouette + DBI）に基づく最適クラスタリングでRAPTORツリーを構築します。