Mobile-agent based distributed fuzzy associative classification rules generation for OLAM

Raghuram, B.; Aghila, G.

doi:10.1109/iama.2009.5228028

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The performance of this method highly depends on the available expert knowledge, especially in high-dimensional problems. In [33], a fuzzy classifier is introduced based on a multi-agent approach. In that work, data is collected from different sources by agents and sent to a central module, where fuzzy rules and a classifier are constructed.…”

Section: A Literature Review On Distributed Fuzzy Rule Miningmentioning

confidence: 99%

“…This structure hence leads to better interpretability and higher accuracy in the classification and decision-making process. The multi-agent design further offers a parallel and fast execution by distributing 'intelligence and decision making' among multiple localities and computational agencies [29][30][31][32][33][34][35][36]. Such an approach aims for better scalability in decision-making and faster learning through efficient knowledge representation and distributed information sharing/computing.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

Sharif

Akbarzadeh-T

2021

Fuzzy Information and Engineering

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INTRODUCTION:With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS:The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.

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Section: A Literature Review On Distributed Fuzzy Rule Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

Sharif

Akbarzadeh-T

2021

Fuzzy Information and Engineering

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“…Irrespective differences in association rule generation approach both models will implemented on distributed environment based up CD approach [5]. An agent based approach for mining fuzzy association rule based classifier presented in [11] but model is based up on centralized approach the major problem of this approach is huge amount of data as to transfer from all sources to centralized source. While all these methods are based on CD approaches, the accurate and communication efficient FDM approach based fuzzy associative classification for distributed databases for horizontal portioning proposed in [12] and for vertical partitioning proposed in [13].…”

Section: Related Workmentioning

confidence: 99%

Incremental associative classification on distributed databases

Bhukya

Gyani

2014

International Conference for Convergence for Technology-2014

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Distributed Data Mining (DDM) which is a process of extracting knowledge from distributed data without integrating them in a common database. Due to its vast application in real world application distributed data mining has been a most familiar research interest. As the associative classification technique proved to be most efficient classifier compare to other classifiers we can found certain proposals in literature which can perform associative classification over distributed databases. Even after incremental data mining proved to be most optimized way to upgrade mined rules when new set of transaction added to database, there are lack of proposals which can perform incremental mining over distributed databases. Considering these issues the article presents incremental associative classification model over horizontally distributed databases. Experimental conducted using synthesized datasets has shown encouraging results.

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Privacy preserving associative classification on vertically partitioned databases

Raghuram¹,

Gyani²

2012

2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

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Mobile-agent based distributed fuzzy associative classification rules generation for OLAM

Cited by 5 publications

References 7 publications

Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

Incremental associative classification on distributed databases

Privacy preserving associative classification on vertically partitioned databases

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