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CRISP-DM

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Architects Research Society
Architects Research Society
Sep 25, 2016 · Big Data

Overview of Data Mining Tasks, Processes, and Related Machine Learning Techniques

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.

Big DataCRISP-DMKDD
0 likes · 8 min read
Overview of Data Mining Tasks, Processes, and Related Machine Learning Techniques
Qunar Tech Salon
Qunar Tech Salon
Aug 14, 2015 · Big Data

The Nine Laws of Data Mining: Principles, Processes, and Insights

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.

CRISP-DMbusiness knowledgedata mining
0 likes · 19 min read
The Nine Laws of Data Mining: Principles, Processes, and Insights