1 / 10

Data Mining with Oracle using Classification and Clustering Algorithms

Data Mining with Oracle using Classification and Clustering Algorithms. Presented by Nhamo Mdzingwa Supervisor: John Ebden. Overview of Presentation. Recap of Proposal Classification of Data Mining & DM Algorithms Oracle Data Mining Data Mining Process Evaluation of Results

megan-cabrera
Download Presentation

Data Mining with Oracle using Classification and Clustering Algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Mining with Oracle using Classification and Clustering Algorithms Presented by Nhamo Mdzingwa Supervisor: John Ebden

  2. Overview of Presentation • Recap of Proposal • Classification of Data Mining & DM Algorithms • Oracle Data Mining • Data Mining Process • Evaluation of Results • Progress so far • Updated Timeline • Plans

  3. Objective • Investigate two types of algorithms available in Oracle10g for data mining (ODM). • Apply the two algorithms to actual data. • Analyse & • Evaluate results in terms of performance.

  4. Classification of Data Mining • Directed data mining/supervised learning which build a model that describes one particular attribute in terms of the rest of the data. • Undirected DM / Unsupervised learning builds a model to establish the relationships amongst all the input attributes by grouping.

  5. Input attributes but have no output attributes Input attributes and output one or more attributes Classification of Data Mining algorithms DM strategies Unsupervised learning Supervised learning Classification Naive Bayes Model Seeker Adaptive Bayes Clustering k-Means O-Cluster Estimation Association Discovery Prediction Predictive variance Visualization

  6. Algorithms offered in Oracle10g classification • Adaptive Bayes Network • Naive Bayes • Model Seeker clustering • k-Means • O-Cluster • Predictive variance association rules • Apriori (association rules)

  7. Evaluation of Results • Evaluation of unsupervised learning models involves determining the level of predictive accuracy. • Evaluated using test data sets. • Compare confidence and support levels of models created from the same training data to determine accuracy.

  8. Progress • Literature Survey • Oracle10g installed on Athena in Hons Lab • Exploring the Oracle9i and 10g Suite including JDeveloper • Member of MetaLink (Oracle’s online support service)

  9. Updated Timeline

More Related