DTSA 5504 Data Mining Pipeline Lecture Summary Data Science Exam Prep Guide 2025/ 2026

DTSA 5504 - Data Mining Pipeline – lecture summary.pdf DTSA 5504 - Data Mining Pipeline – lecture summary.pdf DTSA 5504 - Data Mining Pipeline – lecture summary.pdf




DTSA 5504 - Data Mining
Pipeline – lecture summary




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- Data Mining Pipeline – lecture summary.pdf DTSA 5504 - Data Mining Pipeline – lecture summary.pdf DTSA 5504 - Data Mining Pipeline – lecture summary.pdf

,DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf




Why Data Mining? Explosive data growth (in KB, MB, GB,TB, PB, EB, and ZB)


What is data mining? Knowledge discovery from data (Extraction of interesting patterns or
knowledge from huge amounts of data.)


Benefits of data mining Scalability and efficiency


The four views of data mining Data, Application, Knowledge, Technique


What are the 5Vs of Data Mining? Volume, Variety, Velocity, Veracity, Value


Relational, transactional data (Data View) E.g., student records, bank accounts, store purchases


Sequential, temporal, streaming data (Data View) E.g., gene sequences, stock prices, sensor readings


Spatial, spatial-temporal data (Data View) E.g., land use, bird migration, traffic condition


Text, multimedia, Web data (Data View) E.g., news articles, audio/video/image data, hypertext


DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf

, DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf




Graph, network data (Data View) E.g., social network, power grid, co-authorship


Market Analysis, target advertisement (Application E.g., customer profiling, product recommendation
View)


Healthcare, medical research (Application View) E.g., disease diagnosis, patient care, drug discovery


Science and engineering (Application View) E.g., air pollution, marine life, electric vehicles


Security (Application View) E.g., surveillance, intrusion/crime, fraud, cyberattack


Government, nonprofit (Application View) E.g., urban planning, traffic control, education


Frequent pattern , correlation (Knowledge View) E.g., Songs listened together or in certain sequence


Categorization (Knowledge View) E.g., Similarity among user with certain purchases, differences between two
patient groups


Anomaly, outliers (Knowledge View) E.g., sensor errors, fraud activities, extreme events


DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf DTSA 5504 - Data Mining Pipeline.pdf