MAD-ARM: Mobile agent based distributed association rule mining

Raja, A.Saleem; Raj, E. George Dharma Prakash

doi:10.1109/iccci.2013.6466112

Cited by 9 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The item sets are always upgrading on remote sites at the immobile agent. [3] The association rules have been recently recognized as an important tool for knowledge discovery in databases. The problem of discovering association rules was first investigated in pioneering work in [4].…”

Section: -3 Association Rule Miningmentioning

confidence: 99%

Association Rules Elicitation for Customer’s Shopping on E-commerce Applications

Nouri,

Gaafar

2024

IJCI

View full text Add to dashboard Cite

This research provides an approach for eliciting association rules for customers’ shopping on e-commerce applications based on the Apriori algorithm. The proposed system shows and displays some offers and deals from various branches. The system provides the analytics for the seller as the demand for some varieties and the weakness in other varieties and the whole application is organized on the Cloud. The architecture includes three levels; the front-end, middle, and back-end. The front-end level of the site-based Mobile shopping application is made up of Android Mobile devices, to buy miscellaneous products from various nearby branches. The front-end level also displays the link between items purchased. The middle repository level provides a Web service to generate returns from a relational database. The Exchanged information and data between application and servers is stored in the Cloud. The background level offers a Web server and a MySQL database. In this paper, we propose an architecture that reduces the communication overhead in existing Mobile Agent-based Distributed Association Rule Mining (MAD-ARM).

show abstract

Section: -3 Association Rule Miningmentioning

confidence: 99%

Association Rules Elicitation for Customer’s Shopping on E-commerce Applications

Nouri,

Gaafar

2024

IJCI

View full text Add to dashboard Cite

show abstract

“…Mobile information agents have the ability to travel automatically over the Internet. Such agents can allow, for example, dynamic load balancing in largescale networks, a reduction in data transfer between information server applications, and small business logic migration with middle-range enterprise search intranets [8,[12][13][14][15][16].…”

Section: Classification Of Information Agentsmentioning

confidence: 99%

Intelligent agents system for medical information communication

Evtimova-Gardair¹,

Pallis

2021

Intelligent Wireless Communications

View full text Add to dashboard Cite

This chapter describes the importance of the agents when making an intelligent web communication system for medical information. The state-of-the-art of the agents is performed and the implementation of mobile agents is highlighted with their positive and negative features when gathering information in the Internet. In this respect, an intelligent system with a mobile agent model for communication of medical information is proposed that could be implemented in the wireless structure. The platform for the creation of the medical communication information system that is proposed is a part of intelligent wireless communication. The result from using mobile agent is generated which is also important when working with big data.

show abstract

“…In [11], A.Saleem Raja, offer overview of Agent based distributed DM architecture; Distributed AR Mining algorithms and view of prevailing agent sourced ARM and specify issues in prevailing architecture. At last, author provides architecture termed MAD-ARM, tried to eliminate communication overhead and guarantee MA security.…”

Section: Related Workmentioning

confidence: 99%

Mobi-X Architecture Modelling for Mobile Agent using Association Pattern Mining

Priyadharshini*¹,

Narayani²

2019

IJRTE

View full text Add to dashboard Cite

In a mobile agent system, if agents’ functionality can be assessed and evaluated between peers of environmental modelling, it can reduce the exploration burden of unvisited states and unseen situations, thus an effectual learning process has to be accelerated. So as to construct an accurate and effectual model in certain time period is a significant problem, specifically in complex environment. To overcome this crisis, the investigation anticipates a model based data mining approach based on tree structure to achieve co-ordination amongst the mobile agent, effectual modelling and less memory utilization. The anticipated model suggests Mobi-X architecture to mobile agent system with a tree structure for effectual modelling. The construction of tree for real time mobile agent system is utilized to generate virtual experiences like elapse time during mining of tree structure. In addition, this model is appropriate for knowledge mining. This work is inspired by knowledge mining concept in mobile agent systems where an agent can built a global model from scattered local model held by individual agents. Subsequently, it increases modelling accuracy to offer valid simulation outcome for indirect learning at initial stage of mining. In order to simplify mining procedure, this anticipated model relies on re-sampling approach with associative rule mining to grafting branches of constructed tree. The tree structure provides the functions of mobile agents with useful experience from peer to peer connectivity, indeed of combining all the available agents. The simulation outcomes shows that proposed re-sampling can attain efficiency and accelerate the functionality of mobile agents based cooperation applications.

show abstract

MAD-ARM: Mobile agent based distributed association rule mining

Cited by 9 publications

References 8 publications

Association Rules Elicitation for Customer’s Shopping on E-commerce Applications

Association Rules Elicitation for Customer’s Shopping on E-commerce Applications

Intelligent agents system for medical information communication

Mobi-X Architecture Modelling for Mobile Agent using Association Pattern Mining

Contact Info

Product

Resources

About