Name
Abstract | ||
|---|---|---|
This paper applies the social network theory to analyze the FOODMART sales dataset which is from a large supermarket company in the United States. We first measure the node degree distribution, the average path length and the clustering coefficient. The results show that the basket network accords with the characteristics of a small world network, but its topology is different from a number of actual large social networks. Its point degree distribution follows a Poisson distribution rather than a power-law distribution. We then try to find the cliques in the network and conclude that products which have same attributes connect more closely each other than the products which have different attributes. Furthermore, we also find that family members with similar age structure buy the similar products. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICNC.2013.6818140 | 2013 NINTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC) |
Keywords | Field | DocType |
shopping basket analysis, produce network, cliques | Average path length,Mathematical optimization,Social network,Computer science,Small-world network,Degree distribution,Poisson distribution,Clustering coefficient | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Qi | 1 | 0 | 0.34 |
| Shaohui Ma | 2 | 0 | 1.01 |
| Yisheng Dai | 3 | 0 | 0.34 |