A Study of Association Rules Mining Algorithms Based on Adaptive Support

2010 ◽  
Vol 108-111 ◽  
pp. 436-440
Author(s):  
Yue Shun He ◽  
Ping Du
Keyword(s):  
Association Rules ◽  
Efficient Method ◽  
User Needs ◽  
Association Rules Mining ◽  
Minimum Support ◽  
The Core ◽  
Minimum Number ◽  
Adaptive Support ◽  
Mining Algorithms ◽  
Specific Number

This paper presents an adaptive support for Boolean algorithm for mining association rules, the Algorithm does not require minimum support from outside, in the mining process of the algorithm will be based on user needs the minimum number of rules automatically adjust the scope of support to produce the specific number of rules, the algorithm number of rules for the user needs to generate the rules to a certain extent, reduce excavation time, avoid the artificial blindness specified minimum support. In addition, the core of the algorithm is using an efficient method of Boolean-type mining, using the logical OR, AND, and XOR operations to generate association rules, to avoided the candidate itemsets generated In the mining process, and only need to scan the database once, so the algorithm has a certain efficiency.

A New Algorithm of Association Rules Mining Based on Relation Matrix

2011 ◽  
Vol 179-180 ◽  
pp. 55-59
Author(s):  
Ping Shui Wang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Automatic Extraction ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Research Areas ◽  
Mining Algorithms ◽  
Relation Matrix

Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.

Association Rules Mining over Data Streams: Review

2012 ◽  
Vol 256-259 ◽  
pp. 2890-2893
Author(s):  
Jun Tan
Keyword(s):  
Association Rules ◽  
Data Streams ◽  
High Speed ◽  
Future Research ◽  
Research Directions ◽  
Association Rules Mining ◽  
Mining Algorithm ◽  
Future Research Directions ◽  
Mining Algorithms ◽  
Scanning Algorithm

Data streams are continuous, unbounded and coming with high speed which put forward a strong challenge against traditional association rules mining algorithms. In this paper, we give a comprehensive summary on association rules mining algorithm from three side including single-pass scanning algorithm, data processing model, memory optimization. At last, we discuss the main problems and future research directions.

An Efficient Graph-Based Method for Parallel Mining Problems

2004 ◽  
Vol 03 (02) ◽  
pp. 143-154
Author(s):  
Chin-Chen Chang ◽  
Chih-Yang Lin ◽  
Pei-Yu Lin
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Load Balance ◽  
Experimental Results ◽  
Memory Usage ◽  
Association Rules Mining ◽  
Parallel Mining ◽  
Mining Algorithms

Parallel association rules mining is a noticeable problem in data mining. However, little work has been proposed to deal with three important issues: (1) less memory usage; (2) less communication, among the involved computers, over the network; and (3) load balance among computers. In this paper, we present a graph-based scheme to solve the parallel mining problem by applying independent groups (clusters of maximal cliques). To bring the three issues to a close, the purpose of the independent groups aims at dividing a database into several independent sub-databases, so each sub-database can be employed independently to perform mining algorithms. To emphasis the effectiveness of the graph-based scheme, we adopt the independent groups not only for maximal large itemsets mining but also for general large itemsets mining. The experimental results show that our scheme can improve the efficiency for parallel mining when the independent groups are well-organized and designed.

scholarly journals Algoritma Apriori Untuk Penentuan Assosiasi Penjualan Barang

2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Calvin Ivan Wiryawan ◽  
Yustina Retno Wahyu Utami ◽  
Didik Nugroho
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Basic Needs ◽  
Marketing Strategies ◽  
Apriori Algorithm ◽  
Growing Up ◽  
Minimum Support ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The increasing of selling basic needs make the company has to provide a lot of goods. The data will be growing up with increasing the transaction at Sari Bumi store. All this time, the selling basic needs at Sari Bumi Store unstructured well so that needed an application with produce important information that can decide marketing strategies. In this research, Apriori algorithm is used to determine association rules. This method was chosen because it is one of the classic data mining algorithms to look for patterns of relationships between one or more items in one dataset. A priori algorithms can help companies in developing marketing strategies. The result of this research is combination between 4 item set with a minimum support of 30% and minimum confidence of 60%.Keywords: sale, staple, apriori algorithm

Weighted Association Rules Mining Algorithm Research

2012 ◽  
Vol 241-244 ◽  
pp. 1598-1601
Author(s):  
Jun Tan
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Mining Algorithm ◽  
Pruning Strategy ◽  
Mining Algorithms

Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

Multi-Level Association Rules Mining Algorithm Based on Semantic Relativity

2011 ◽  
Vol 460-461 ◽  
pp. 363-368
Author(s):  
Lei Zhang ◽  
Zhi Chao Wang
Keyword(s):  
Association Rules ◽  
Domain Knowledge ◽  
Semantic Meaning ◽  
Association Rules Mining ◽  
Minimum Support ◽  
Mining Algorithm ◽  
Multi Level

Traditional multi-level association rules mining approaches are based only on database contents. The relations of items in itemset are considered rarely. It leads to generate a lot of meaningless itemsets. Aiming at the problem,multi-level association rules mining algorithm based on semantic relativity is proposed. Domain knowledge is described by Ontology. Every item is seen as a concept in Ontology. Semantic relativity is used to measure the semantic meaning of itemsets. Minimum support of itemset is set according to its length and semantic relativity. Semantic related minimum support with length-decrease is defined to filter meaningless itemsets. Experiments results showed that the method in the paper can improve the efficiency of multi-level association rules mining and generated meaningful rules.

Research on Parallel Association Rules Mining Algorithm Based on Hadoop

2014 ◽  
Vol 543-547 ◽  
pp. 3625-3631
Author(s):  
Shao Rong Feng ◽  
Lin Bao Ye ◽  
Zi Yu Lin
Keyword(s):  
Cloud Computing ◽  
Association Rules ◽  
Random Access ◽  
Comprehensive Analysis ◽  
Frequent Itemset ◽  
Association Rules Mining ◽  
Computing Platform ◽  
Mining Algorithm ◽  
Cloud Computing Platform ◽  
Mining Algorithms

The purpose of association rules mining is to find rules which can meet the minimum support and minimum confidence from a large quantity of data. To find the valid association rules efficiently, we had a comprehensive analysis on some well-know parallel association rules mining algorithms and proposes a new parallel association rules mining algorithm (Array Based on Hadoop, short for ABH) based on the cloud computing platform. The ABH scans the database only once, uses the 0/1 array to represent one of the transactions and to record the frequency of the same transaction. Moreover, by utilizing the random access characteristics of the array and the special nature of the frequent itemset, the ABH can reduce the quantity of frequent candidate itemset effectively and find the frequent itemset quickly. We have compared the ABH with two classical algorithms CD and DD through experiment; we can find that ABH outperforms CD and DD.

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