Lecture 01 Data Mining

Mar 11, 200830 likes13,662 views

Pier Luca Lanzi

Course Data Mining and Text Mining 2007/2008 Prof. Pier Luca Lanzi Politecnico di Milano Data Mining

This document summarizes Chapter 6 of the book "Data Mining: Concepts and Techniques" which discusses frequent pattern mining. It introduces basic concepts like frequent itemsets and association rules. It then describes several scalable algorithms for mining frequent itemsets, including Apriori, FP-Growth, and ECLAT. It also discusses optimizations to Apriori like partitioning the database and techniques to reduce the number of candidates and database scans.

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This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques

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This document discusses association rule mining. Association rule mining finds frequent patterns, associations, correlations, or causal structures among items in transaction databases. The Apriori algorithm is commonly used to find frequent itemsets and generate association rules. It works by iteratively joining frequent itemsets from the previous pass to generate candidates, and then pruning the candidates that have infrequent subsets. Various techniques can improve the efficiency of Apriori, such as hashing to count itemsets and pruning transactions that don't contain frequent itemsets. Alternative approaches like FP-growth compress the database into a tree structure to avoid costly scans and candidate generation. The document also discusses mining multilevel, multidimensional, and quantitative association rules.

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Text mining presentation in Data mining AreaMahamudHasanCSE

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DMTM Lecture 08 Classification rulesPier Luca Lanzi

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Data Driven Game Design @ Campus Party 2018Pier Luca Lanzi

GGJ18 al Politecnico di Milano - Presentazione che precede la presentazione d...Pier Luca Lanzi

GGJ18 al Politecnico di Milano - Presentazione di aperturaPier Luca Lanzi

Presentation for UNITECH event - January 8, 2018Pier Luca Lanzi

DMTM Lecture 20 Data preparationPier Luca Lanzi

DMTM Lecture 19 Data explorationPier Luca Lanzi

DMTM Lecture 18 Graph miningPier Luca Lanzi

DMTM Lecture 17 Text miningPier Luca Lanzi

DMTM Lecture 16 Association rulesPier Luca Lanzi

DMTM Lecture 15 Clustering evaluationPier Luca Lanzi

DMTM Lecture 14 Density based clusteringPier Luca Lanzi

DMTM Lecture 13 Representative based clusteringPier Luca Lanzi

DMTM Lecture 12 Hierarchical clusteringPier Luca Lanzi

DMTM Lecture 11 ClusteringPier Luca Lanzi

DMTM Lecture 10 Classification ensemblesPier Luca Lanzi

DMTM Lecture 09 Other classificationmethodsPier Luca Lanzi

DMTM Lecture 08 Classification rulesPier Luca Lanzi

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