The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniq...The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.展开更多
Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out us...Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out usingassociation rule mining or community detection approach. This article usesboth methods to investigate a transaction dataset collected from a brick-andmortargrocery store. The findings reveal interesting purchasing patterns oflocal residents and prompt us to consider dynamic modeling of the productnetwork in the future.展开更多
Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation...Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users,as they get abundant preferred information such as cheaper rates,amazing offers,discounts,comparison of similar products,etc.over their smartphones or laptops hence they straightaway place order instead of walking down to showroom.As a result,large companies such as Tesco,Wal-Mart,Target,etc.have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket.The data which are generated from consumer purchase pattern,Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis.This research work discusses various improved association rule mining(ARM)algorithms.The objective of this study is to identify gaps,providing opportunities for new research,to recognize expansion of Big Data analytics with retail environment and its future directions.This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform.Further various use cases highlighting the need of‘Big Data Retail Analytics’are discussed for emerging trends to promote sales and revenues,to keep check on competitor’s websites,comparison of various brands,enticing new customers.展开更多
Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,...Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters.展开更多
文摘The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.
文摘Finding meaningful sets of co-purchased products allows retailers to manageinventory better and develop market strategies. Analyzing the baskets ofproducts, known as market basket analysis, is typically carried out usingassociation rule mining or community detection approach. This article usesboth methods to investigate a transaction dataset collected from a brick-andmortargrocery store. The findings reveal interesting purchasing patterns oflocal residents and prompt us to consider dynamic modeling of the productnetwork in the future.
文摘Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users,as they get abundant preferred information such as cheaper rates,amazing offers,discounts,comparison of similar products,etc.over their smartphones or laptops hence they straightaway place order instead of walking down to showroom.As a result,large companies such as Tesco,Wal-Mart,Target,etc.have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket.The data which are generated from consumer purchase pattern,Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis.This research work discusses various improved association rule mining(ARM)algorithms.The objective of this study is to identify gaps,providing opportunities for new research,to recognize expansion of Big Data analytics with retail environment and its future directions.This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform.Further various use cases highlighting the need of‘Big Data Retail Analytics’are discussed for emerging trends to promote sales and revenues,to keep check on competitor’s websites,comparison of various brands,enticing new customers.
文摘Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters.
