The article introduces time‑series data, its ubiquitous examples, the challenges of traditional small models, and proposes a universal time‑series large model that simplifies data preparation and model building, ultimately enabling more efficient and stable industrial AI solutions, now offered as a cloud service.
This article explains the two most common data‑mining frameworks—CRISP‑DM and SEMMA—detailing their six and five stages respectively, illustrating each phase with diagrams and highlighting how the iterative nature of data mining drives continuous improvement.
Data mining, an interdisciplinary field of computer science, involves tasks such as anomaly detection, clustering, classification, and regression, follows standardized processes like KDD, CRISP-DM, and SEMMA, and often leverages machine learning techniques—including supervised, unsupervised, and reinforcement learning—to extract valuable insights from complex datasets.
This article presents nine fundamental laws of data mining—covering goals, knowledge, preparation, experimentation, patterns, insight, prediction, value, and change—explaining how business objectives and domain expertise drive each stage of the CRISP‑DM process and why technical metrics alone cannot guarantee success.
