Data mining 3160714 Syllabus Download With Weightage

Data mining 3160714 Syllabus Download With Weightage

Data mining 3160714 Syllabus is a term that refers to Computer Department covers this subject This year, this Subject is covered in the 6th Semester. 

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Introduction to data mining (DM):

Motivation for Data Mining – Data Mining-Definition and Functionalities –
Classification of DM Systems – DM task primitives – Integration of a Data
Mining system with a Database or a Data Warehouse – Issues in DM – KDD


Data Pre-processing:

Data summarization, data cleaning, data integration and transformation, data
reduction, data discretization and concept hierarchy generation, feature
extraction , feature transformation, feature selection, introduction to
Dimensionality Reduction, CUR decomposition


Concept Description, Mining Frequent Patterns, Associations and

What is concept description? – Data Generalization and summarization-based
characterization – Attribute relevance – class comparisons, Basic concept,
efficient and scalable frequent item-set mining methods, mining various kind
of association rules, from association mining to correlation analysis,
Advanced Association Rule Techniques, Measuring the Quality of Rules.


Classification and Prediction:

Classification vs. prediction, Issues regarding classification and prediction,

Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-
Based Algorithms, Neural Network-Based Algorithms, Rule-Based

Algorithms, Combining Techniques, accuracy and error measures, evaluation
of the accuracy of a classifier or predictor. Neural Network Prediction
methods: Linear and nonlinear regression, Logistic Regression Introduction of
tools such as DB Miner / WEKA / DTREG DM Tools


Cluster Analysis:

Clustering: Problem Definition, Clustering Overview, Evaluation of

Clustering Algorithms, Partitioning Clustering -K-Means Algorithm, K-
Means Additional issues, PAM Algorithm; Hierarchical Clustering –

Agglomerative Methods and divisive methods, Basic Agglomerative Hierarchical Clustering, Strengths and Weakness; Outlier Detection,
Clustering high dimensional data, clustering Graph and Network data.


Web mining and other data mining:

Web Mining: Introduction to Web Mining, Web content mining, Web usage
mining, Web Structure mining, Web log structure and issues regarding web
logs, Spatial Data Mining, Temporal Mining, And Multimedia Mining.
Applications of Distributed and parallel Data Mining.


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