Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Garg, Deepak; Sharma, Hemant

doi:10.14569/specialissue.2011.010323

Cited by 6 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The major weakness of Apriori algorithm is producing large number of candidate itemsets and large number of database scans which is equal to maximum length of frequent itemset [5]. It is very much expensive to scan large database [11]. A true reason of apriori failure is it lacks efficient processing method on database [7].…”

Section: Resultsmentioning

confidence: 99%

Comparing the Performance of Frequent Pattern Mining Algorithms

Garg¹,

Kumar²

2013

IJCA

View full text Add to dashboard Cite

Frequent pattern mining is the widely researched field in data mining because of it's importance in many real life applications. Many algorithms are used to mine frequent patterns which gives different performance on different datasets. Apriori, Eclat and FP Growth are the initial basic algorithm used for frequent pattern mining. The premise of this paper is to find major issues/challenges related to algorithms used for frequent pattern mining with respect to transactional database. General TermsAlgorithms.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparing the Performance of Frequent Pattern Mining Algorithms

Garg¹,

Kumar²

2013

IJCA

View full text Add to dashboard Cite

show abstract

“…The first step is mining the trend of continues attribute through cycle curve and the second step is calculating the period of the attribute. MPTAR did not define the cumulative threshold, and it is short of embracing upcoming transaction entries in the association rule mining [15,18,25].…”

Section: Related Workmentioning

confidence: 99%

“…The time period during which a fact is true with respect to the real world is considered as valid time and the time period during which a fact is stored in the database is called transaction time. According to these two time aspects temporal databases allow the division of three different forms [4,25]. They are a.…”

Section: Introductionmentioning

confidence: 99%

Mining Time Variant Frequent Pattern using PPM and PWM: A Comparison

Shirsath¹,

Verma²

2013

IJCA

View full text Add to dashboard Cite

The process of exploring and analyzing data from different perspective, using automatic or semiautomatic techniques is called Data mining. Data mining extracts knowledge or useful information and discovers correlations or meaningful patterns and rules from large databases [1,2]. Using these patterns and rules it is possible for business enterprises to identify new and unexpected trends, subtle relations in the data and use them to increase revenue and cut cost. In this paper we proposed a comparative study over Progressive Partition Miner (PPM) and Progressive Weighted miner (PWM).

show abstract

Dimensionality reduction for association rule mining with IST-EFP algorithm

Siswanto

Shaufiah

2015

2015 3rd International Conference on Information and Communication Technology (ICoICT)

View full text Add to dashboard Cite

Comparative Analysis of Various Approaches Used in Frequent Pattern Mining

Cited by 6 publications

References 25 publications

Comparing the Performance of Frequent Pattern Mining Algorithms

Comparing the Performance of Frequent Pattern Mining Algorithms

Mining Time Variant Frequent Pattern using PPM and PWM: A Comparison

Dimensionality reduction for association rule mining with IST-EFP algorithm

Contact Info

Product

Resources

About