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Data Mining

Association Rule Learning

Anomaly Detection Methods

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Clustering Algorithms

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Data Mining Project Lifecycle

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Data Mining and Customer Relationship Management (CRM)

Data Mining for Fraud Detection

Data Mining in Finance

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Ensuring Data Privacy in Data Mining

Ethical Aspects of Data Mining

Frequent Pattern Mining Algorithms

Machine Learning vs Data Mining

Mining Complex Data Types

Multimedia Data Mining

Predictive Analytics Fundamentals

Time Series Analysis in Data Mining

Web Mining Methods

Social Media Data Mining

Spatial Data Mining

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Association Rule Learning

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Generalized Sequential Pattern (GSP) Algorithm

Key Characteristics: GSP is an algorithm for sequential pattern mining that handles time constraints, taxonomies (hierarchical relationships), and itemsets of various lengths. Example Use Case: Sequence analysis in retail, like identifying sequences of products purchased consecutively over periods of time.

RuleGrowth Algorithm

Key Characteristics: RuleGrowth is an algorithm designed for discovering sequential rules in sequences. This algorithm can identify rules with confidence and support above user-specified thresholds. Example Use Case: Detecting patterns in customer shopping sequences to optimize product placement.

FP-Growth Algorithm

Key Characteristics: The FP-Growth Algorithm is used for mining the complete set of frequent itemsets without candidate generation, using a compressed representation of the database called the FP-tree (Frequent Pattern Tree). Example Use Case: Analyzing website browsing patterns to improve webpage layout and user experience.

Apriori Algorithm

Key Characteristics: This is an influential algorithm for mining frequent itemsets for boolean association rules. It uses a breadth-first search strategy to count the support of itemsets and uses a candidate generation function which exploits the downward closure property of support. Example Use Case: Market Basket Analysis to identify customer purchase patterns by finding associations between different items purchased.

Eclat Algorithm

Key Characteristics: Eclat stands for Equivalence Class Clustering and bottom-up Lattice Traversal. It is a depth-first search algorithm that uses a vertical database format. Eclat counts common prefixes to improve efficiency over Apriori. Example Use Case: Identifying gene co-expression patterns in bioinformatics data.

CMRules Algorithm

Key Characteristics: CMRules is an algorithm for mining associative classification rules within a transactional database. It focuses on identifying class association rules (CARs) for classification purposes. Example Use Case: Customer segmentation for personalized marketing campaigns based on transaction data.

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