A New Association Rules Mining Algorithms Based on Directed Itemsets Graph

2007 ◽  
pp. 660-663 ◽  
Author(s):  
Lei Wen ◽  
Minqiang Li
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
New Association ◽  
Mining Algorithms

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 FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3091
Author(s):  
Hong-Jun Jang ◽  
Yeongwook Yang ◽  
Ji Su Park ◽  
Byoungwook Kim
Keyword(s):  
Association Rules ◽  
Spatial Data ◽  
Association Rule ◽  
Clustering Algorithm ◽  
Frequent Pattern ◽  
Pattern Growth ◽  
Position Data ◽  
New Association ◽  
Iot Devices ◽  
Mining Algorithms

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.

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.

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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