Introduction
Learn what data mining is, its core functions, techniques, applications and challenges.
📚 Programming Tutorial
Data Mining
Data mining is a process used to extract valuable information from large sets of data. It is the practice of examining large pre-existing databases in order to generate new information.
Data Mining Tutorial Index
27 Topics⛏️ Data Mining
Attribute Types
Understand nominal, binary, ordinal and numeric attribute types.
Basic Statistical Descriptions of Data
Learn the measures of central tendency and dispersion used to describe data.
Knowledge Discovery from Data (KDD)
Understand the KDD process: data selection, transformation, mining and evaluation.
Data Mining architecture
Learn the components of a data mining system: data sources, warehouse server, mining engine and more.
Types of Data
Learn the kinds of data that can be mined.
Data Mining Functionalities
Learn descriptive and predictive functionalities such as classification, clustering and outlier analysis.
Classification of Data Mining systems
Learn how data mining systems are classified by data, knowledge, technique and application.
Data mining Task primitives
Understand the primitives used to specify a data mining task or query.
Integration of Data mining system with a Data warehouse
Learn the schemes for coupling a data mining system with a data warehouse.
Major issues in Data Mining
Learn the main challenges in mining methodology, performance and diverse data types.
Data Preprocessing
Learn data cleaning, integration, reduction and transformation.
🔗 Association Rule Mining
Mining Frequent Patterns
Learn frequent itemsets and the basics of association rule mining.
Associations and Correlations
Learn about Associations and Correlations.
Mining Methods
Learn methods such as Apriori and FP-growth for mining frequent patterns.
Apriori Algorithm
Learn methods such as Apriori for mining frequent patterns.
FP-Growth Algorithm
Learn methods such as FP-growth for mining frequent patterns.
Mining Various kinds of Association Rules
Learn multilevel, multidimensional and other kinds of association rules.
Correlation Analysis
Learn how correlation measures show the strength of association rules.
Constraint based Association mining
Learn how user constraints guide and speed up association mining.
Graph Pattern Mining
Learn how frequent patterns are mined from graph data.
SPM(Sequential Pattern Mining)
Learn how frequent ordered sequences are found in sequence data.
🗂️ Classification
Basic Concepts
Learn classification process and phases in classification.
Decision tree induction
Learn how decision trees are built from training data.
Bayesian classification
Learn classification using Bayes theorem and naive Bayes.
Rule–based classification
Learn how IF-THEN rules are used for classification.
Lazy learner(K-NN)
Learn how classification using K-NN algorithm.
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