Parallel and distributed frequent-regular patternmining using vertical format in large databases

Kumar, G. Suresh; Kumari, Vatsavayi Valli

doi:10.1049/cp.2012.2506

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…[Vijay Kumar 2011, [Vijay Kumar 2012, [15,16] developed many algorithms towards regular itemset mining. The regular itemset describes the pattern occurrence frequency as well as occurrence behavior.…”

Section: [Shengnan Cong 2005] [10]mentioning

confidence: 99%

Mining Distributed Databases for Negative Associations from Regular and Frequent Patterns

NVS¹

2019

IJATCSE

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Most of the transactional data stored in distributed databases, especially when region wise business conducted. Extensive knowledge about the business is hidden in these databases, the discovery of which will give immense power to the decisionmakers. Most of the decisions are taken around frequently occurring patterns and the positive associations that exist among those patterns. Mining a single database can yield the patterns of sales happening at different distributed locations. The regularity of the occurrence of sales pattern is equally as important as frequent patterns. Negative associations also reveal interesting discovery about the business in addition to positive associations and yet time negative associations among the regularly and frequently occurring patterns having a heavy bearing on the Business. It is also important to study the occurrence of the patterns at all the distributed locations and determine the regularity or global nature of the patterns.In this paper, Algorithms presented using which mining of negative association that exist among regular and frequent patterns mined presented considering both local and Global Locations.

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Section: [Shengnan Cong 2005] [10]mentioning

confidence: 99%

Mining Distributed Databases for Negative Associations from Regular and Frequent Patterns

NVS¹

2019

IJATCSE

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“…With this mining of popular patterns in incremental database have been proposed and have been implemented. In the paper [13,14] downward closure property has been discussed which is applicable for mining Popular patterns from the database. We have to check the downward closure property for every pattern provided in the database.…”

Section: Related Workmentioning

confidence: 99%

“…A suffix tree algorithm has been proposed in the paper [10] to notice symbol, order and section periodicity in time series. In the paper [14] a new techniquecalled PFRP-methods are proposed to determineregular patterns [16] in large databases. This helps us to find the regularity [17] of each pattern in the database.…”

Section: Related Workmentioning

confidence: 99%

Incremental Mining of Popular Patterns from Transactional Databases

Kumar¹,

Sreedevi²,

Bhargav³

et al. 2018

IJET

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From the day the mining of frequent pattern problem has been introduced the researchers have extended the frequent patterns to various helpful patterns like cyclic, periodic, regular patterns in emerging databases. In this paper, we get to know about popular pattern which gives the Popularity of every items between the incremental databases. The method that used for the mining of popular patterns is known as Incrpop-growth algorithm. Incrpop-tree structure is been applied in this algorithm. In incremental databases the event recurrence and the event conduct of the example changes at whatever point a little arrangement of new exchanges are added to the database. In this way proposes another calculation called Incrpop-tree to mine mainstream designs in incremental value-based database utilizing Incrpop-tree structure. At long last analyses have been done and comes about are indicated which gives data about conservativeness, time proficient and space productive.

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“…The most imperative what's more, aggressive territory in information mining and learning disclosure look into is to mine different intriguing patterns [13]. In the paper [14] we came to realize that consistency of thing set and recurrence of thing set both are similarly imperative on any sort of database. An-other calculation has been proposed RFRP to locate the general successive examples of a thing set.…”

Section: Introductionmentioning

confidence: 99%

Regular frequent crime pattern mining on crime datasets

Kumar¹,

Sreedevi²,

Krishna³

et al. 2018

IJET

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The objective of violation information mining is to comprehend different violation designs in criminal conduct in request to foresee viola-tions and expect criminal movement to stay away from the violation not to happen. Foreseeing violation is one of the worldwide difficulties looking by Law authorization office and it requires tireless endeavors with a specific end goal to limit. In this paper we are presenting anoth-er violation design called general incessant violation design which happens frequently at certain time interims utilizing vertical information arrange additionally fulfills descending conclusion property. Violation designs were not characterized by insights and its distinguishing proof is some-thing other than checking and abridging violations that are comparable in attributes and additionally area on a guide. Violation design is a gathering of at least one violations answered to or on the other hand found by the police.

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Parallel and distributed frequent-regular patternmining using vertical format in large databases

Cited by 8 publications

References 10 publications

Mining Distributed Databases for Negative Associations from Regular and Frequent Patterns

Mining Distributed Databases for Negative Associations from Regular and Frequent Patterns

Incremental Mining of Popular Patterns from Transactional Databases

Regular frequent crime pattern mining on crime datasets

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