| Unit I |
Introduction to data mining |
8 |
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- What is Data Mining? |
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- What is the Data Mining Process? |
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- Basic Data Mining Tasks |
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- Problem Identification |
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- Data Mining Metrics |
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- Data Cleaning (pre-processing, feature selection, |
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data reduction, feature encoding, noise and |
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missing values, etc.) |
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- Key Issues |
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- Opportunities for Data Mining |
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| Unit II |
Mining frequent patterns, associations and correlations |
8 |
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- Basic concepts |
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- Efficient and scalable frequent itemset mining |
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algorithms |
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- Mining various kinds of association rules |
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(multilevel and multidimensional) |
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- Association rule mining versus correlation analysis |
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- Constraint-based association mining |
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| Unit III |
Classification and prediction |
8 |
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- Definition |
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- Decision tree induction |
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- Bayesian classification |
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- Rule-based classification |
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- Classification by backpropagation and support vector |
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machines |
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- Associative classification |
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- Lazy learners |
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- Prediction |
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- Accuracy and error measures |
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| Unit IV |
Testing and Implementation |
8 |
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- Cluster analysis |
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- Definition |
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- Clustering algorithms (partitioning, hierarchical, |
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density-based, grid-based, and model-based) |
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- Clustering high-dimensional data |
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- Constraint-based cluster analysis |
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- Outlier analysis (density-based and distance-based) |
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| Unit V |
Project Management |
8 |
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- Data mining on complex data and applications |
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- Algorithms for mining of spatial data, multimedia |
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data, text data |
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- Data mining applications |
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- Social impacts of data mining |
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- Trends in data mining |
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