Data Mining Guidelines and Practical List PDF

Data Mining Guidelines and Practical List

Course Objective: This course introduces data mining techniques and enables students to apply these techniques on real-life datasets. The course focuses on three main data mining techniques: Classification, Clustering and Association Rule Mining tasks.

Data Mining Guidelines

Introduction to Data Mining – Applications of data mining, data mining tasks, motivation and challenges, types of data attributes and measurements, data quality.

Data Pre-processing – aggregation, sampling, dimensionality reduction, Feature Subset Selection, Feature Creation, Discretization and Binarization, Variable Transformation.

Classification: Basic Concepts, Decision Tree Classifier: Decision tree algorithm, attribute selection measures, Nearest Neighbour Classifier, Bayes Theorem and Naive Bayes Classifier,

Model Evaluation: Holdout Method, Random Sub Sampling, Cross-Validation, evaluation metrics, confusion matrix.

Association rule mining: Transaction data-set, Frequent Itemset, Support measure, Apriori Principle, Apriori Algorithm, Computational Complexity, Rule Generation, Confidence of association rule.

Cluster Analysis: Basic Concepts, Different Types of Clustering Methods, Different Types of Clusters, K-means: The Basic K-means Algorithm, Strengths and Weaknesses of K-means algorithm, Agglomerative Hierarchical Clustering: Basic Algorithm, Proximity between clusters,

DBSCAN: The DBSCAN Algorithm, Strengths and Weaknesses.

Data Mining Reference Books:

  1. Han, J., Kamber, M.,& Jian,P. (2011). Data Mining: Concepts and Techniques. 3rd edition. Morgan Kaufmann
  2. Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. 1st Edition. Pearson Education


Download Data Mining Guidelines and Programs List PDF

Data Mining Guidelines and Programs List PDF

Data Mining Guidelines and Programs List
Delhi University (DU)

Data Mining Guidelines and Programs List PDF

Data Mining Compiled Book
Delhi University (DU)