This report details a university big‑data project that builds a data‑driven framework for assessing lithium‑ion battery storage health, cleaning operational data, detecting abnormal cells with DBSCAN, and predicting SOC/SOH using LightGBM, while highlighting findings, limitations, and future improvements.
This article explains how Alibaba tackles anomaly detection for tens of millions of metrics in a 100‑thousand‑machine cluster by comparing vertical time‑series methods with horizontal clustering, choosing DBSCAN for large‑scale monitoring, and detailing the ETL, computation, and visualization pipeline.
This article presents a Python project that reads point‑cloud data from CSV files, applies DBSCAN and KMeans clustering, extracts cluster features, and uses the Hungarian algorithm to match clusters across frames for tracking, complete with full source code and result visualization.
DBSCAN is a density‑based clustering algorithm that automatically discovers arbitrarily shaped clusters and isolates noise, with detailed explanations of core, border, and noise points, step‑by‑step examples, Python implementations using scikit‑learn, and guidance on key parameters such as eps and min_samples.
The article describes JD Search's machine‑learning alert system that combines offline and real‑time training, FFT‑based periodic detection, Prophet forecasting, and DBSCAN anomaly clustering, and explains architectural design, data preprocessing, model optimization, and distributed deployment to improve alert accuracy and response speed.
The article describes JD Search’s machine‑learning‑based time‑series forecasting and anomaly‑detection platform, detailing its overall architecture, offline and real‑time training pipelines, FFT‑based periodicity detection, Prophet forecasting, DBSCAN outlier detection, and distributed optimizations such as Alink integration and load‑balancing strategies.
This article explains the DBSCAN density‑based clustering algorithm, its core concepts, parameters, and step‑by‑step implementation, then shows how dynamically derived eps values enable the algorithm to group design‑draft modules for automatic front‑end code generation, improving development efficiency.
This talk presents the design and implementation of 58's commercial recruitment recommendation system, covering the business scenario, system architecture, regional and behavior‑based recall methods, various ranking models—including coarse‑ranking, dual‑tower, DIN‑bias, and multitask W3DA—and future optimization directions.
This article presents a comprehensive overview of the 58.com commercial recruitment recommendation system, detailing its business challenges, system architecture, region‑based and behavior‑based recall strategies, coarse‑ and fine‑ranking models, bias handling, evaluation methods, and future directions.
This article details the challenges of root‑cause identification in high‑availability securities trading platforms and presents two intelligent‑operations solutions—DBSCAN‑based clustering and Bayesian inference—to quickly locate anomalies and improve recovery efficiency.
In the iQIYI Multimodal Person Recognition Challenge, I leveraged the provided facial features, weighted face‑quality averaging, DBSCAN‑based noise clustering and a dynamic extra noise class within an iterative KNN‑to‑neural‑network training pipeline, ultimately reaching the top‑5 and open‑sourcing the full workflow on GitHub.
The article describes how the Super Bus platform leverages unsupervised DBSCAN clustering and a Graham‑scan convex‑hull algorithm, combined with a data‑center and distributed processing framework, to automatically generate compliant service zones that match user demand while improving efficiency and scalability.
This article explains how to apply the DBSCAN clustering algorithm to geolocated user event data and leverage Apache Spark’s distributed processing with PairRDDs to efficiently identify frequent user regions, detect outliers, and build location‑based services such as personalized recommendations and security alerts.
This article describes how Netflix’s technical operations team built an automatic anomaly‑detection system based on the DBSCAN clustering algorithm to identify subtly failing servers from time‑series error‑rate data, evaluate its effectiveness, and discuss practical deployment considerations.
