Mining Association Rules: A Case Study on Benchmark Dense Data (original) (raw)

Mining Dense Data: Association Rule Discovery on Benchmark Case Study

Jurnal Teknologi, 2015

Data Mining (DM), is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. In this article, we present comparison result between Apriori and FP-Growth algorithm in generating association rules based on a benchmark data from frequent itemset mining data repository. Experimentation with the two (2) algorithms are done in Rapid Miner 5.3.007 and the performance result is shown as a comparison. The results obtained confirmed and verified the results from the previous works done.

Identification of Best Algorithm in Association Rule Mining Based on Performance

Data Mining finds hidden pattern in data sets and association between the patterns. To achieve the objective of data mining association rule mining is one of the important techniques. Association rule mining is a particularly well studied field in data mining given its importance as a building block in many data analytics tasks. Many studies have focused on efficiency because the data to be mined is typically very large. This paper presents a comparison on three different association rule mining algorithms i.e. FP Growth, Apriori and Eclat. The time required for generating frequent itemsets plays an important role. This paper describes implementations of these three algorithms that use several optimizations to achieve maximum performance, w.r.t. execution time. The comparison of algorithms based on the aspects like different support and confidence values.

A Survey on Association Rule Mining Algorithms for Frequent Itemsets

— These days many current data mining tasks are accomplished successfully only in discovery of Association rule. It appeals more attention in frequent pattern mining because of its wide applicability. Many researchers successfully presented several efficient algorithms with its performances in the area of rule generation. This paper mainly assembles a theoretical survey of the existing algorithms. Here author provides the considered Association rule mining algorithms by beginning an overview of some of the latest research works done on this area. Finally, discusses and concludes the merits and limitation.

A New Association Rule Mining Based on Frequent Item Set

Computer Science Conference Proceedings, 2011

In this paper a new mining algorithm is defined based on frequent item set. Apriori Algorithm scans the database every time when it finds the frequent item set so it is very time consuming and at each step it generates candidate item set. So for large databases it takes lots of space to store candidate item set. The defined algorithm scans the database at the start only once and then makes the undirected item set graph. From this graph by considering minimum support it finds the frequent item set and by considering the minimum confidence it generates the association rule. If database and minimum support is changed, the new algorithm finds the new frequent items by scanning undirected item set graph. That is why it's executing efficiency is improved distinctly compared to traditional algorithm.

Research on Association Rule Mining Algorithms

International Journal of Advanced Research in Computer and Communication Engineering, 2022

Association rule mining is one of the important part in the field of data mining. The scope of association rule mining is very broad. In association rules mining, frequent item sets mining is essential. Apriori algorithm, Eclat algorithm and FP-growth algorithm are famous algorithms to find frequent item sets. This algorithm can reduce the database and improve mining efficiency.

Data mining: A Reveal on Association Rules Based On Benchmark and eDisiplin Database

Data Mining (DM), is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. In this article, we present comparison result between Apriori and FP-Growth algorithm in generating association rules based on a benchmark data from frequent itemset mining data repository and eDisiplin data from eDisiplin system. The two (2) algorithms are implemented in Rapid Miner 5.3.007 and the performance result is shown as a comparison.

A Comparative Study of Association Rules Mining Algorithms

This paper presents a comparison between classical frequent pattern mining algorithms that use candidate set generation and test and the algorithms without candidate set generation. In order to have some experimental data to sustain this comparison a representative algorithm from both categories mentioned above was chosen (the Apriori, FP-growth and DynFP-growth algorithms). The compared algorithms are presented together with some experimental data that lead to the final conclusions.

Comparative Evaluation of Association Rule Mining Algorithms with Frequent Item Sets

IOSR Journal of Computer Engineering (IOSR-JCE), 2013

This paper represents comparative evaluation of different type of algorithms for association rule mining that works on frequent item sets. Association rule mining between different items in large-scale database is an important data mining problem. Now a day there is lots of algorithms available for association rule mining. To perform comparative study of different algorithms various factor considered in this paper like number of transaction, minimum support and execution time. Comparisons of algorithms are generated based on experimental data which gives final conclusion.

A Comparative Study Of Association Rule Mining Algorithms

2018

Data mining (DM) techniques is the set of algorithms that helps in extracting interesting patterns and previously unknown facts from larger volume of databases. Todays ever changing customer needs, fluctuation business market and large volume of data generated every second has generated the need of managing and analyzing such a large volume of data. Association Rule mining algorithms helps in identifying correlation between two different items purchased by an individual. Apriori Algorithm and FP-Growth Algorithm are the two algorithms for generating Association Rules. This paper aims at analyze the performance of Apriori and FP-Growth based on speed, efficacy and price and will help in understanding which algorithm is better for a particular situation. https://journalnx.com/journal-article/20150659

An Improved Frequent Pattern Tree Based Association Rule Mining Technique

2011 International Conference on Information Science and Applications, 2011

Discovery of association rules among the large number of item sets is considered as an important aspect of data mining. The ever increasing demand of finding pattern from large data enhances the association rule mining. Researchers developed a lot of algorithms and techniques for determining association rules. The main problem is the generation of candidate set. Among the existing techniques, the frequent pattern growth (FP-growth) method is the most efficient and scalable approach. It mines the frequent item set without candidate set generation. The main obstacle of FP growth is, it generates a massive number of conditional FP tree. In this research paper, we proposed a new and improved FP tree with a table and a new algorithm for mining association rules. This algorithm mines all possible frequent item set without generating the conditional FP tree. It also provides the frequency of frequent items, which is used to estimate the desired association rules.