Analysis of Housing Transaction Data

2002 ◽  
Keyword(s):  
Transaction Data

Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

2018 ◽  
Vol 6 (1) ◽  
pp. 41-48
Author(s):  
Santoso Setiawan
Keyword(s):  
Data Mining ◽  
Stock Management ◽  
Transaction Data ◽  
Business People ◽  
Cluster Data ◽  
Data Clusters ◽  
Inventory Strategy ◽  
Transactional Data

Abstract   Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies.   Keywords: inventory, k-means, product transaction data, rapidminer, data mining   Abstrak   Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok.   Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

Using Transaction Data for the Design of Sequential, Multi-unit, Online Auctions

2000 ◽  
Author(s):  
Edieal J. Pinker ◽  
Abraham Seidmann ◽  
Yaniv Vakrat
Keyword(s):  
Online Auctions ◽  
Transaction Data

A Horizon Based Decomposition of Mutual Fund Performance Using Transaction Data

2019 ◽  
Author(s):  
Jules H. van Binsbergen ◽  
Hongxun Ruan ◽  
Ran Xing
Keyword(s):  
Mutual Fund ◽  
Fund Performance ◽  
Mutual Fund Performance ◽  
Transaction Data

Robust Demand Estimation of Choice Models for Personalized Offers Using Sales Transaction Data

2020 ◽  
Author(s):  
Sanghoon Cho ◽  
Mark Ferguson ◽  
Pelin Pekgun ◽  
Jongho Im
Keyword(s):  
Demand Estimation ◽  
Choice Models ◽  
Transaction Data

scholarly journals High-resolution assessment of environmental benefits of dockless bike-sharing systems based on transaction data

2021 ◽  
Vol 296 ◽  
pp. 126423
Author(s):  
Aoyong Li ◽  
Kun Gao ◽  
Pengxiang Zhao ◽  
Xiaobo Qu ◽  
Kay W. Axhausen
Keyword(s):  
High Resolution ◽  
Environmental Benefits ◽  
Transaction Data ◽  
Bike Sharing

Comparing shopper characteristics by online grocery ordering use among households in low-income communities in Maine

2021 ◽  
pp. 1-16
Author(s):  
Laura Y. Zatz ◽  
Alyssa J. Moran ◽  
Rebecca L. Franckle ◽  
Jason P. Block ◽  
Tao Hou ◽  
...  
Keyword(s):  
Low Income ◽  
Grocery Shopping ◽  
Sociodemographic Characteristics ◽  
Chi Square ◽  
Transaction Data ◽  
Future Studies ◽  
Number Of Children ◽  
Online Shoppers ◽  
Race Ethnicity ◽  
Improve Access

Abstract Objective: Online grocery shopping could improve access to healthy food, but it may not be equally accessible to all populations—especially those at higher risk for food insecurity. This study aimed to compare the sociodemographic characteristics of families who ordered groceries online versus those who only shopped in-store. Design: We analyzed enrollment survey and 44 weeks of individually-linked grocery transaction data. We used univariate chi-square and t-tests and logistic regression to assess differences in sociodemographic characteristics between households that only shopped in-store and those that shopped online with curbside pick-up (online only or online and in-store). Setting: Two Maine supermarkets. Participants: 863 parents or caregivers of children under 18 years old enrolled in two fruit and vegetable incentive trials Results: Participants had a total of 32 757 transactions. In univariate assessments, online shoppers had higher incomes (P<0.0001), were less likely to participate in WIC or SNAP (P<0.0001), and were more likely to be female (P=0.04). Most online shoppers were 30–39 years old, and few were 50 years or older (P=0.003). After controlling for age, gender, race/ethnicity, number of children, number of adults, income, and SNAP participation, female primary shoppers (OR=2.75, P=0.003), number of children (OR=1.27, P=0.04), and income (OR=3.91 for 186–300% FPL and OR=6.92 for >300% FPL, P<0.0001) were significantly associated with likelihood of shopping online. Conclusions: In this study of Maine families, low-income shoppers were significantly less likely to utilize online grocery ordering with curbside pick-up. Future studies could focus on elucidating barriers and developing strategies to improve access.

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