Data mining with oracle using classification and clustering algorithms
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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

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Data Mining with Oracle using Classification and Clustering Algorithms

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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

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


Objective

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.


Classification of data mining

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.


Classification of data mining algorithms

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


Algorithms offered in oracle10g

Algorithms offered in Oracle10g

classification

  • Adaptive Bayes Network

  • Naive Bayes

  • Model Seeker

    clustering

  • k-Means

  • O-Cluster

  • Predictive variance

    association rules

  • Apriori (association rules)


Evaluation of results

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.


Progress

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)


Updated timeline

Updated Timeline


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