scholarly journals Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

2012 ◽  
pp. 25-41 ◽  
Author(s):  
Matteo Riondato ◽  
Eli Upfal
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Performance Guarantees

scholarly journals MINING ESSENTIAL RULES USING FREQUENT CLOSED ITEMSETS LATTICE

2009 ◽  
Vol 12 (11) ◽  
pp. 49-56
Author(s):  
Bac Hoai Le ◽  
Bay Dinh Vo
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Suitable Method ◽  
Mining Method ◽  
Parent Child Relationship ◽  
Left Hand ◽  
Child Relationship ◽  
Closed Itemsets ◽  
The Cost ◽  
Parent Child

In traditional mining of association rules, finding all association rules from databases that satisfy minSup and minConf faces with some problems in case of the number of frequent itemsets is large. Thus, it is necessary to have a suitable method for mining fewer rules but they still embrace all rules of traditional mining method. One of the approaches that is the mining method of essential rules: it only keeps the rule that its left hand side is minimal and its right side is maximal (follow in parent-child relationship). In this paper, we propose a new algorithm for mining the essential rules from the frequent closed itemsets lattice to reduce the time of mining rules. We use the parent-child relationship in lattice to reduce the cost of considering parent-child relationship and lead to reduce the time of mining rules.

Mining temporal association rules with frequent itemsets tree

2018 ◽  
Vol 62 ◽  
pp. 817-829 ◽  
Author(s):  
Ling Wang ◽  
Jianyao Meng ◽  
Peipei Xu ◽  
Kaixiang Peng
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Temporal Association

DWFIST

2008 ◽  
pp. 3142-3163
Author(s):  
Rodrigo Salvador Monteiro ◽  
Geraldo Zimbrao ◽  
Holger Schwarz ◽  
Bernhard Mitschang ◽  
Jano Moreira de Souza
Keyword(s):  
Data Warehouse ◽  
Association Rules ◽  
Original Data ◽  
Frequent Itemsets ◽  
Upper And Lower Bounds ◽  
Sufficient Information ◽  
Interestingness Measures ◽  
The Core ◽  
Pattern Retrieval ◽  
Frequency Counts

This chapter presents the core of the DWFIST approach, which is concerned with supporting the analysis and exploration of frequent itemsets and derived patterns, e.g., association rules in transactional datasets. The goal of this new approach is to provide: (1) flexible pattern-retrieval capabilities without requiring the original data during the analysis phase; and (2) a standard modeling for data warehouses of frequent itemsets, allowing an easier development and reuse of tools for analysis and exploration of itemset-based patterns. Instead of storing the original datasets, our approach organizes frequent itemsets holding on different partitions of the original transactions in a data warehouse that retains sufficient information for future analysis. A running example for mining calendar-based patterns on data streams is presented. Staging area tasks are discussed and standard conceptual and logical schemas are presented. Properties of this standard modeling allow retrieval of frequent itemsets holding on any set of partitions, along with upper and lower bounds on their frequency counts. Furthermore, precision guarantees for some interestingness measures of association rules are provided as well.

Closed-Itemset Incremental-Mining Problem

2011 ◽  
pp. 150-153
Author(s):  
Luminita Dumitriu
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Incremental Mining ◽  
Minimum Support ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Mining Association Rules

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.

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