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Knowledge Discovery in a DBMS Data Mining Computing models and finding patterns in large databases current major challenge in database systems & large.

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Presentation on theme: "Knowledge Discovery in a DBMS Data Mining Computing models and finding patterns in large databases current major challenge in database systems & large."— Presentation transcript:

1 Knowledge Discovery in a DBMS Data Mining Computing models and finding patterns in large databases current major challenge in database systems & large data sets Why perform data mining inside a DBMS? Huge data volumes: potentially better results with larger amounts of data; less processing time Minimizes data redundancy; Eliminate proprietary data structures; simplifies data management; security How? Relational algebra, SQL code generation, query optimization, User-defined functions (UDFs), fast export interfaces Contributor: C. Ordonez Email:ordonez@cs.uh.edu

2 Representative problems OLAP cubes in multidimensional data Finding predictive rules Bayesian classification Cluster and correlation discovery Contributor: C. Ordonez Email:ordonez@cs.uh.edu

3 UH’s niche in this area Statistical methods inside a DBMS Dimensionality reduction (PCA, factor analysis) Classification and regression Pattern search (Association rules, OLAP cubes) Applications: Microarray data on cancer patients Medical record analysis (heart/cancer disease) Water pollution Expertise on both Machine learning, statistical algorithms Database Systems Contributor: C. Ordonez Email:ordonez@cs.uh.edu

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