Data Mining
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Follow on Instagram for UpdatesData Mining Syllabus — Unit-wise Content
The official GTU curriculum for Data Mining (subject code 3715507) as prescribed for CE(P · Semester 1 divides the course into 3 units, covering 32 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3715507)Unit 1 Introduction to Data mining principles- Data mining and knowledge
- The need for data mining
- Overview of Data warehousing and mining
- Advantages and challenges
- Data mining applications in various application areas
- Datamarts and OLAP - Data warehouse architectures
- Datawarehouse design
- Steps in Datawarehousing (ETL)
- OLAP Vs OLTP
- Data marts
- design and performance considerations
Unit 2 Data mining Overview, Data mining process, Understanding different types
- of data and preprocessing considerations
- Classification and Prediction - Naive bayes Classification
- back propagation based classification based
- tree based
- Support Vector machines
- Associative classification
- Prediction - Regression Trees
Unit 3 Clustering and association Rule mining, k-means clustering, EM technique,
- Hierarchical Clustering
- Density based methods
- Grid based methods
- Model based methods
- Cluster Analysis and Outlier Analysis
- Association Rule mining - Mining Frequent patterns
- MIning Various associations and rules
- Correlation Analysis
- Apriori ( Market basket Analysis)
- Constraint based association rule mining
- Data mining Applications in Text mining
- Stream mining and Fraud Detection.Working with weka
- Exploring different data sets
- Mining trends
- Case studies
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