Mining high dimensional association rules by generating large frequent k-dimension set

Prasanna, K.; Seetha, M.

doi:10.1109/icdse.2012.6282304

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…It is very important because the database may contain combinations of nominal or quantitative attributes. The process of combining attributes was described in [8]. The equivalent algorithm with some necessary modifications is used.…”

Section: A Preprocessing the Datasetmentioning

confidence: 99%

CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

Prasanna

Seetha²,

Kumar

2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

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At present, due to the developments in Database Technology, large volumes of data are produced by everyday operations and they have introduced the necessity of representing the data in High Dimensional Datasets. Discovering Frequent Determinant Patterns and Association Rules from these High Dimensional Datasets has become very tedious since these databases contain large number of different attributes. For the reason that, it generates extremely large number of redundant rules which makes the algorithms inefficient and it does not fit in main memory.In this paper, a new Association Rule Mining approach is presented, and it efficiently discovers Frequent Determinant Patterns and Association Rules from High Dimensional Datasets. The proposed approach adopts the conventional Apriori algorithm and device anew CApriori algorithm to prune the generated Frequent Determinant Sets effectively. A Frequent Determinant set is selected if its value is first compared with Conviction threshold value and then compared with Support threshold. This double comparison will eliminate the redundancy and generate strong Association Rules.To improve the mining process, this algorithm also makes use of a compressed data structure f_list constructed from feature attributes selected using Heuristic Fitness Function (HFF) and a Heuristic Discretization algorithm. It also makes use of Count Array (CA) devised as One Dimensional Triple Array pair set to minimize main memory utilization. This comprehensive study shows that the approach outperforms with traditional Apriori and obtains more rapid computing speed and at the same time generates Sententious Rules. Further the mining methodology is ascertained to be better in generating strong Association Rules from High Dimensional Databases.

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Section: A Preprocessing the Datasetmentioning

confidence: 99%

CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

Prasanna

Seetha²,

Kumar

2014

2014 International Conference on Science Engineering and Management Research (ICSEMR)

Self Cite

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“…Singh and Agarwal [10] propose a new optimized algorithm and to compare its performance with the existing data mining algorithms. Prasanna and Seetha [11] present a method for generating association rules from large high dimensional data, which can obtain more rapid computing speed and sententious rules. And then, probability-based algorithm, one of an incremental algorithm, is researched by Ariya and Kreesuradej [12], which applies the principle of Bernoulli trial to predict expected frequent item sets for reducing collected border item sets and a number of times to rescan the original database.…”

Section: Introductionmentioning

confidence: 99%

Application of Transaction Mining Based on FP-Table Algorithm in Mobile Electricity Market

Gao¹,

Dai²,

Jiao

2015

JSS

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Electricity market trade based on mobile intelligent device will extend the volume of transaction. For the massive and various trading data, transaction mining algorithm is very useful to find the relationship of correlative elements such as trade price and power capacity, and it always occurs between the power users and power generation enterprises. The novel FP-Table algorithm is proposed in this paper to solve the massive transaction mining problem. The FP-Table algorithm integrates the Hash table into FP-Growth algorithm, using two-dimension table saving frequency count of item pair, then mining the frequency items of electricity transactions efficiently. Application of mobile transaction mining is proved to be high efficiency and high value by performance experiment results.

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Heuristic-Based Clustering Approach for Discovering Colossal Patterns from High-Dimensional Databases

Reddy¹,

Sathya

Nuka

2021

Advances in Automation, Signal Processing, Instrumentation, and Control

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Mining high dimensional association rules by generating large frequent k-dimension set

Cited by 6 publications

References 9 publications

CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

CApriori: Conviction based Apriori algorithm for discovering frequent determinant patterns from high dimensional datasets

Application of Transaction Mining Based on FP-Table Algorithm in Mobile Electricity Market

Heuristic-Based Clustering Approach for Discovering Colossal Patterns from High-Dimensional Databases

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