Clustering Experiments on Big Transaction Data for Market Segmentation

Singh, Ashishkumar Rajpal; Rumantir, Grace Widjaja; South, Annie; Bethwaite, Blair

doi:10.1145/2640087.2644161

Cited by 8 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Blockchain is considered to be an ideal solution to these problems. Based on this, we try to apply our signature scheme to the transactions of big data [27].…”

Section: Application Of Signatures Schemementioning

confidence: 99%

Research on a New Signature Scheme on Blockchain

Yuan

2017

Security and Communication Networks

View full text Add to dashboard Cite

With the rise of Bitcoin, blockchain which is the core technology of Bitcoin has received increasing attention. Privacy preserving and performance on blockchain are two research points in academia and business, but there are still some unresolved issues in both respects. An aggregate signature scheme is a digital signature that supports making signatures on many different messages generated by many different users. Using aggregate signature, the size of the signature could be shortened by compressing multiple signatures into a single signature. In this paper, a new signature scheme for transactions on blockchain based on the aggregate signature was proposed. It was worth noting that elliptic curve discrete logarithm problem and bilinear maps played major roles in our signature scheme. And the security properties of our signature scheme were proved. In our signature scheme, the amount will be hidden especially in the transactions which contain multiple inputs and outputs. Additionally, the size of the signature on transaction is constant regardless of the number of inputs and outputs that the transaction contains, which can improve the performance of signature. Finally, we gave an application scenario for our signature scheme which aims to achieve the transactions of big data on blockchain.

show abstract

“…Blockchain is considered to be an ideal solution to these problems. Based on this, we try to apply our signature scheme to the transactions of big data [27].…”

Section: Application Of Signatures Schemementioning

confidence: 99%

Research on a New Signature Scheme on Blockchain

Yuan

2017

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Hence, based on the volume of the EFTPOS data set used, this project can be categorised as a Big Data project. Our experience in acquiring, secured-storing and processing the commercial in confidence EFTPOS data has been reported in Singh, Rumantir, South & Bethwaite (2014).…”

Section: Figure 1: Two Tiered Market Segmentation Frameworkmentioning

confidence: 99%

“…This high volume of data makes even the basis operations, such as calculating the total monetary amount of each retailer from the data set, very time consuming and resource intensive. Strategies to overcome this Big Data problem is reported in Singh, Rumantir, South & Bethwaite (2014).…”

Section: Clustering Experimentsmentioning

confidence: 99%

“…For this work, parallelisation in the form of multi-threading using a cluster of Intel Xeon and AMD Opteron CPUs of various clock speeds with 16 cores, 32 GB RAM and 500GB hard disk size is employed. Our experience in acquiring, secured-storing and processing the commercial in confidence EFTPOS data is reported in Singh, Rumantir, South & Bethwaite (2014). …”

Section: Clustering Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

Singh

Rumantir

2015

AJIS

Self Cite

View full text Add to dashboard Cite

Almost all the papers reported in the literatures on market segmentation modeling using retail transaction data deal with finding groupings of customers. This paper proposes the application of clustering and classification techniques for finding groupings of retailers who use the Electronic Funds Transfer at Point of Sale (EFTPOS) facilities of a major bank in Australia in their businesses. The RFM (Recency, Frequency, Monetary value) analysis on each retailer is used to reduce the large data set of customer purchases through the EFTPOS network into attributes that may explain the business activities of the retailers. Preliminary results published in Singh, show that groupings of retailers with distinct combinations of RFM values can be established. Encouraged by the promising business insights gained from the clustering experiments using the RFM values, in this paper, we report our findings in extracting business rules pertinent to each cluster. For this purpose, we incorporate attributes of the EFTPOS transaction data in addition to the derived RFM attributes to build a decision tree to facilitate the extraction of the business rules.

show abstract

A Two Tiered Finite Mixture Modelling Framework to Cluster Customers on EFTPOS Network

Jin

Rumantir

2015

AI 2015: Advances in Artificial Intelligence

View full text Add to dashboard Cite

Clustering Experiments on Big Transaction Data for Market Segmentation

Cited by 8 publications

References 17 publications

Research on a New Signature Scheme on Blockchain

Research on a New Signature Scheme on Blockchain

Two-tiered Clustering Classification Experiments for Market Segmentation of EFTPOS Retailers

A Two Tiered Finite Mixture Modelling Framework to Cluster Customers on EFTPOS Network

Contact Info

Product

Resources

About