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Computer Science

Data Mining

Association Rule Learning

Anomaly Detection Methods

Big Data Technologies

Clustering Algorithms

Classification Algorithms

Data Dimensionality Reduction Techniques

Data Mining Project Lifecycle

Data Mining Success Metrics

Data Mining Techniques

Data Mining and Customer Relationship Management (CRM)

Data Mining for Fraud Detection

Data Mining in Finance

Data Quality Issues

Data Mining in Retail

Data Mining in Healthcare

Data Mining Research Topics

Data Mining Tools and Software

Data Mining Skill Set

Data Mining Case Studies

Data Preprocessing Techniques

Data Mining Challenges

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

Text Mining Techniques

Frequent Pattern Mining Algorithms

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Apriori

Apriori is an algorithm used to identify frequent itemsets and relevant association rules. It operates by generating candidate sets and uses a minimum support threshold to identify frequent itemsets. An example of its use is in market basket analysis for discovering common item combinations in shopping carts.

FP-Growth

FP-Growth stands for 'Frequent Pattern Growth' and is an algorithm that directly mines the frequent itemsets without candidate generation, using a compact data structure called the FP-tree. An example of its use is mining customer purchase patterns from transaction databases without generating huge candidate sets.

HMine

HMine algorithm is used for mining frequent itemsets but optimizes the process by a hash-based approach to reduce the search space. An example of its use is for mining frequent patterns in sales data to identify commonly bought items together.

Eclat

ECLAT (Equivalence CLass Transformation) is an algorithm that uses a vertical data format to mine frequent itemsets. It represents the dataset using tidlists and performs an intersection to find commonalities. An example of its use is in genomic sequence analysis to find common patterns across DNA sequences.

SPADE (Sequential PAttern Discovery using Equivalence classes)

SPADE is an algorithm that facilitates the discovery of sequential patterns by utilizing lattice search techniques and simple join operations. It can handle large databases efficiently. An example of use is in telecommunication for analyzing patterns in call sequences.

GSP (Generalized Sequential Pattern)

GSP is an algorithm designed for mining sequential patterns. It extends the Apriori principle by considering sequences of itemsets. An example of its use is identifying frequently followed paths in a website's clickstream data.

PrefixSpan

PrefixSpan (Prefix-projected Sequential Pattern mining) is an algorithm for discovering sequential patterns in a dataset by exploring prefix sub-patterns. It avoids costly candidate generation. An example of its use is in text mining for finding frequent subsequences of terms or words in a collection of documents.

CARMA (Continuous Association Rule Mining Algorithm)

CARMA is an algorithm designed for mining association rules in a continuous streaming environment. It efficiently updates frequent itemsets without having to rescan the entire data stream. An example of its use is in monitoring systems for detecting frequent occurrences of event combinations.

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