scholarly journals Research of Improved FP-Growth Algorithm in Association Rules Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yi Zeng ◽  
Shiqun Yin ◽  
Jiangyue Liu ◽  
Miao Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Experimental Results ◽  
Frequent Pattern ◽  
Association Rules Mining ◽  
Classical Algorithm ◽  
Pattern Growth ◽  
Data Volume ◽  
Better Than

Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.

Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm

2012 ◽  
Vol 3 (2) ◽  
pp. 24-41 ◽  
Author(s):  
Tutut Herawan ◽  
Prima Vitasari ◽  
Zailani Abdullah
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Teaching Strategies ◽  
Engineering Students ◽  
Mining Method ◽  
Association Rules Mining ◽  
Quality Of Learning ◽  
Pattern Growth

One of the most popular techniques used in data mining applications is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, called SLP-Growth (Significant Least Pattern Growth) for capturing interesting rules from students suffering mathematics and examination anxieties datasets. The datasets were taken from a survey exploring study anxieties among engineering students in Universiti Malaysia Pahang (UMP). The results of this research provide useful information for educators to make decisions on their students more accurately and adapt their teaching strategies accordingly. It also can assist students in handling their fear of mathematics and examination and increase the quality of learning.

FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases

2005 ◽  
Vol 1 (3) ◽  
pp. 129-135
Author(s):  
Jun Luo ◽  
Sanguthevar Rajasekaran
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fast Algorithm ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Important Data ◽  
Computational Performance ◽  
Large Databases ◽  
Intersection Operation ◽  
Better Than

Association rules mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attributes lists using hash Tables (FIT) is presented. FIT is designed for efficiently computing all the frequent itemsets in large databases. It deploys an idea similar to Eclat but has a much better computational performance than Eclat due to two reasons: 1) FIT makes fewer total number of comparisons for each intersection operation between two attributes lists, and 2) FIT significantly reduces the total number of intersection operations. Our experimental results demonstrate that the performance of FIT is much better than that of Eclat and Apriori algorithms.

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.

A Novel and Improved Apriori Algorithm

2014 ◽  
Vol 721 ◽  
pp. 543-546 ◽  
Author(s):  
Dong Juan Gu ◽  
Lei Xia
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Inner Product ◽  
Apriori Algorithm ◽  
Product Operation ◽  
Classical Algorithm ◽  
Transaction Database ◽  
The Given ◽  
To Receive ◽  
Improved Algorithm

Apriori algorithm is the classical algorithm in data mining association rules. Because the Apriori algorithm needs scan database for many times, it runs too slowly. In order to improve the running efficiency, this paper improves the Apriori algorithm based on the Apriori analysis. The improved idea is that it transforms the transaction database into corresponding 0-1 matrix. Whose each vector and subsequent vector does inner product operation to receive support. And comparing with the given minsupport, the rows and columns will be deleted if vector are less than the minsupport, so as to reduce the size of the rating matrix, improve the running speeding. Because the improved algorithm only needs to scan the database once when running, therefore the running speeding is more quickly. The experiment also shows that this improved algorithm is efficient and feasible.

Research on Intelligent Recommendation Method and its Application on Internet Bookstore

2010 ◽  
Vol 121-122 ◽  
pp. 447-452
Author(s):  
Qing Zhang Chen ◽  
Yu Jie Pei ◽  
Yan Jin ◽  
Li Yan Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet

As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

Application of Association Rules Mining in Employment Guidance

2012 ◽  
Vol 479-481 ◽  
pp. 129-132
Author(s):  
Lei Wang ◽  
Cun Xiao Yi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Important Task ◽  
Complex Data ◽  
Employment Rate ◽  
Association Rules Mining ◽  
Mining Association Rules ◽  
Vocational Colleges

How to improve the employment rate of graduates is an important task for higher vocational colleges to solve. In order to effectively improve their Employment competitiveness, advice should be made to help students to enhance specific kinds of learning and ability. Association Rules Mining is one core of the Data Mining Association Rules, it’s helpful in finding useful information hidden in complex data. By using Association Rules Mining in finding the knowledge and ability which helps employed students to earn their jobs, necessary ability for each kind of job can be found, and then advice offered for students to target their employment career will be more exact and proper.

Research and Simulation of Data Mining Method Based on the Association Rules Algorithm

2014 ◽  
Vol 651-653 ◽  
pp. 2185-2188
Author(s):  
Jin Ping Zou ◽  
Xiao Dong Xie
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rules ◽  
Experimental Results ◽  
Mining Method ◽  
Accurate Data ◽  
Minimum Support ◽  
Data Mining Method

the accurate data mining problem is studied in this paper. With the increasing of data attributes, degree of complexity of the data storage is also increased, resulting in that in data mining process, the complexity of computation is too high, reducing the convergence of the data mining method, thereby reducing the efficiency of data mining. To this end, this paper presents a data mining method based on association rules algorithm. The data is made simplified processing, to obtain the association rules between data which provides the basis for data mining. According to the association rules between the data, the data in line with the minimum support degree is calculated, to achieve accurate data mining. Experimental results show that the proposed algorithm for data mining, can improve mining efficiency, and achieve the desired results.

Research into the Algorithm of Frequent Pattern Mining Based on across Linker

2012 ◽  
Vol 195-196 ◽  
pp. 984-986
Author(s):  
Ming Ru Zhao ◽  
Yuan Sun ◽  
Jian Guo ◽  
Ping Ping Dong
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Important Data ◽  
Classical Algorithm ◽  
Frequent Itemsets Mining ◽  
Mining Frequent Itemsets

Frequent itemsets mining is an important data mining task and a focused theme in data mining research. Apriori algorithm is one of the most important algorithm of mining frequent itemsets. However, the Apriori algorithm scans the database too many times, so its efficiency is relatively low. The paper has therefore conducted a research on the mining frequent itemsets algorithm based on a across linker. Through comparing with the classical algorithm, the improved algorithm has obvious advantages.

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