An efficient approach for mining association rules from high utility itemsets

Sahoo, Jayakrushna; Das, Ashok Kumar; Goswami, Adrijit

doi:10.1016/j.eswa.2015.02.051

Cited by 77 publications

(41 citation statements)

References 34 publications

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“…X→ ┐Y(S,C) ┐X → Y(S,C) ┐X→ ┐Y(S,C) P 12 ,P 13 s=3,c=0.75,1 s=2,c=1 -P 12 , P 16 --s=2,c=1,0.67 P 13 , P 14 s=3,c=1,0.6 -P 13 , P 16 s=3,c=1,1 s=3,c=1,1 -P 14 , P 15 s=3,c=0.6,1 --Given: A set of session t = { 1 , 2 ,.., q }, ∀ t=1…..n, a set of pages P i = {P 1 ,P 2 ,…,P m }, The sample pages are calculated for explain the negative rules. We have taken sample set of pages then, perform the associations between different pages.…”

Section: Pagesmentioning

confidence: 99%

Extracting of Positive and Negative Association Rules

Gandhimathi

Anbazhagan

2018

IJERMT

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Abstractssociation rules analysis is a basic technique to expose how items/patterns are associated to each other. There are two common ways to measure association such as Support and Confidence. Several methods have been proposed in the literature to diminish the number of extracted association rules. Association Rule Mining is one of the greatest current data mining techniques designed to group objects together from huge databases aiming to take out the motivating correlation and relation with massive quantity of data. Association rule mining is used to discover the associated patterns from datasets. In this paper, we propose association rules from new methods on web usage mining. Generally, web usage log structure has several records so we have to overcome those unwanted records from large dataset. First of all the pre-processed data from the NASA dataset is clustered by the popular K-Means algorithm. Subsequently, the matrix calculation is progressed on that data. Further, the associations are performed on filtered data and get rid of the final associated page results. Positive and negative association rules are gathered by using new algorithm with Annul Object ( ). Wherever the object " " is presented those rules are known as negative association rule. Otherwise, the rules are positive association rules.

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Section: Pagesmentioning

confidence: 99%

Extracting of Positive and Negative Association Rules

Gandhimathi

Anbazhagan

2018

IJERMT

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“…This approach helps to resolve the issue of large numbers of candidates being generated, and thus has better performance in both runtime and memory consumption. Sahoo et al (2015) investigated integrating the closure property into HUIM and proposed the HUCI-Miner algorithm to mine all CHUIs and their HUGs from HUIs. This is one of the first studies on extracting CHUIs and HUGs on the mined HUIs from a database.…”

Section: Chuis and Generators Miningmentioning

confidence: 99%

“…While this is considered as an efficient approach, it generates a large number of HUIs, and this algorithm requires much space for processing as well as fails to terminate. Sahoo et al (2015) proposed a Fast High Utility Itemset Miner (FHIM) and defined the promising utility co-occurrence structure to further reduce the number of candidates. Zida et al (2016) proposed the EFIM algorithm for fast HUIM.…”

Section: High Utility Itemset Miningmentioning

confidence: 99%

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An efficient approach for mining closed high utility itemsets and generators

Mai¹,

Nguyen

2017

Journal of Information and Telecommunication

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Mining closed high utility itemsets (CHUIs) serves as a compact and lossless representation of high utility itemsets (HUIs). CHUIs and their generators are useful in analytical and recommendation systems. In this paper, we introduce a lattice approach to extract CHUIs and their generators from a set of HUIs quickly. The experimental results show that mining CHUIs and their generators from a lattice of HUIs is efficient in both runtime and memory usage. ARTICLE HISTORY

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“…The concept of reliability stems from the description of the product's reliability, which refers to the capability that enables the product to complete the required function under a\specified condition and within the specified time (Sinha, 1986). Current research on reliability and association rule tends to consider rule mining based on statistical data, such as the product's basic reliability (Jayakrushna and Ashok, 2015) and useful life (Xu, 2008); or assess the data quality for mining using some reliability methods (Katja and M arc-Thorsten, 2011;Sandro, Benny and Ian, 2005). However, current research rarely provides objective and quantitative judgment on the reliability of the association rule.…”

Section: Introductionmentioning

confidence: 99%

A Reliability Evaluation System of Association Rules

Chen¹,

Feng²,

Luo³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABS TRACT:In mining association rules, the evaluation of the rules is a highly important work because it directly affects the usability and applicability of the output results of mining. In this paper, the concept of reliability was imported into the association rule evaluation. The reliability of association rules was defined as the accordance degree that reflects the rules of the mining data set. Such degree contains three levels of measurement, namely, accuracy, completeness, and consistency of rules. To show its effectiveness, the "accuracy-completeness-consistency" reliability evaluation system was applied to two extremely different data sets, namely, a basket simulation data set and a multi-source lightning data fusion. Results show that the reliability evaluation system works well in both simulation data set and the actual problem. The three-dimensional reliability evaluation can effectively detect the useless rules to be screened out and add the missing rules thereby improving the reliability of mining results. Furthermore, the proposed reliability evaluation system is applicable to many research fields; using the system in the analysis can facilitate obtainment of more accurate, complete, and consistent association rules.

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An efficient approach for mining association rules from high utility itemsets

Cited by 77 publications

References 34 publications

Extracting of Positive and Negative Association Rules

Extracting of Positive and Negative Association Rules

An efficient approach for mining closed high utility itemsets and generators

A Reliability Evaluation System of Association Rules

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