Synthesizing heavy association rules from different real data sources

Adhikari, Avishek; Rao, P. Venkateswara

doi:10.1016/j.patrec.2007.09.001

Cited by 48 publications

(33 citation statements)

References 10 publications

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“…Execution time comparison between our proposed approach and Animesh approach [13], Upper support = 35%, Lower Support = 15%, Upper Confidence = 35% and Lower Confidence = 15 %. Figure 5 shows a comparison between Existing Animesh approach [13] which discussed in section 2.4 and the proposed approach processing time when adding additional records on databases contains different number of transactions (30000, 120000, and 450000 Transaction). Experiment in figure 5 executed based on upper support with value 35%, lower support with value 15%, Upper Confidence with value 35%, and lower Confidence with Value 15%.…”

Section: Experimental Results and Evaluationmentioning

confidence: 99%

“…Animesh proposed [13] an approach for generating global rules from local rules exists in local databases. Anamish approach has four interfaces used to generate global rules from local rules as shown in Figure 3.…”

Section: Synthesizing Global Patterns From Local Patterns [13]mentioning

confidence: 99%

“…An incremental approach for maintaining association rules for distributed databases is proposed. It integrates the FUP concepts [5], association-rule maintenance algorithm for record modification [12] and Synthesizing association rules from different data sources [13]. This is an advanced approach to maintain incremental mining in distributed databases.…”

Section: Proposed Approachmentioning

confidence: 99%

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An efficient incremental maintenance for association rules mining based on distributed databases

Darwish

Elgohery

Badr

et al. 2013

Proceedings of 2013 3rd International Conference on Computer Science and Network Technology

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Data Mining helps to reveal very important patterns and associations in large databases. Discovering these associations is very important to decision makers. Many organizations have multiple distributed databases and it is important to discover and maintain its association rule. Any update in the database requires starting the whole mining process again to generate new rules. This requires rescanning the whole database to maintain rules. This paper presents an efficient approach to reduce the time of incremental mining for association rules in distributed databases. The proposed approach is Apriori based distributed formulation on message passing interface. It uses the concept of pre large concept to help in reducing the cost of incremental mining. Experiments prove that our proposed incremental approach is much efficient than existing approaches. We present comparative analysis between the proposed approach and existing approach.

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Section: Experimental Results and Evaluationmentioning

confidence: 99%

Section: Synthesizing Global Patterns From Local Patterns [13]mentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

See 1 more Smart Citation

An efficient incremental maintenance for association rules mining based on distributed databases

Darwish

Elgohery

Badr

et al. 2013

Proceedings of 2013 3rd International Conference on Computer Science and Network Technology

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show abstract

“…Furthermore, many works [2,3,4,5,6,7,8,26] have been proposed to improve the global synthesizing process. Their principles are to analyze the local frequent patterns at different sites, in order to discover other new and useful patterns.…”

Section: Introductionmentioning

confidence: 99%

“…It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [2,3,4,5,6,7,8,26] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results (i.e., the results that would be obtained if all of the databases are put together and mining has been done).…”

mentioning

confidence: 99%

Probabilistic Models for Local Patterns Analysis

Khiat¹,

Belbachir²,

Ahmed³

2014

Journal of Information Processing Systems

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Abstract-Recently, many large organizations have multiple data sources (MDS') distributed over different branches of an interstate company. Local patterns analysis has become an effective strategy for MDS mining in national and international organizations. It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [2,3,4,5,6,7,8,26] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results (i.e., the results that would be obtained if all of the databases are put together and mining has been done). When the pattern is present in the site, but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore, this process can lose some interesting patterns, which can help the decider to make the right decision. In such situations we propose the application of a probabilistic model in the synthesizing process. An adequate choice for a probabilistic model can improve the quality of patterns that have been discovered. In this paper, we perform a comprehensive study on various probabilistic models that can be applied in the synthesizing process and we choose and improve one of them that works to ameliorate the synthesizing results. Finally, some experiments are presented in public database in order to improve the efficiency of our proposed synthesizing method.

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A survey on mining multiple data sources

Ramkumar

Hariharan²,

Selvamuthukumaran

2012

WIREs Data Min & Knowl

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Advancements in computer and communication technologies demand new perceptions of distributed computing environments and development of distributed data sources for storing voluminous amount of data. In such circumstances, mining multiple data sources for extracting useful patterns of significance is being considered as a challenging task within the data mining community. The domain, multi‐database mining (MDM) is regarded as a promising research area as evidenced by numerous research attempts in the recent past. The methods exist for discovering knowledge from multiple data sources, they fall into two wide categories, namely (1) mono‐database mining and (2) local pattern analysis. The main intent of the survey is to explain the idea behind those approaches and consolidate the research contributions along with their significance and limitations. This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Synthesizing heavy association rules from different real data sources

Cited by 48 publications

References 10 publications

An efficient incremental maintenance for association rules mining based on distributed databases

An efficient incremental maintenance for association rules mining based on distributed databases

Probabilistic Models for Local Patterns Analysis

A survey on mining multiple data sources

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