Applying Process Mining in Manufacturing and Logistic for Large Transaction Data

2018 ◽  
pp. 378-388 ◽  
Author(s):  
Wacharawan Intayoad ◽  
Till Becker
Keyword(s):  
Process Mining ◽  
Transaction Data

Discovering Customer Paths from Location Data with Process Mining

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Onur Dogan
Keyword(s):  
Process Mining ◽  
Location 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.

Analysis of Housing Transaction Data

2002 ◽  
Keyword(s):  
Transaction Data

Process Mining - A Comprehensive Review

2018 ◽  
Vol 6 (7) ◽  
pp. 1108-1113
Author(s):  
S. Vijayarani ◽  
A. Sakila ◽  
R. Ramya
Keyword(s):  
Process Mining ◽  
Comprehensive Review

Process Mining im Prototypenbau

2019 ◽  
Vol 114 (11) ◽  
pp. 707-710
Author(s):  
Günther Schuh ◽  
Jan-Philipp Prote ◽  
Andreas Gützlaff ◽  
Sven Cremer ◽  
Seth Schmitz
Keyword(s):  
Process Mining

Process mining algorithm to discover non-certain choice and parallel relation

2009 ◽  
Vol 28 (11) ◽  
pp. 2922-2925 ◽  
Author(s):  
Hong-mei HUANG ◽  
Yun ZHANG
Keyword(s):  
Process Mining ◽  
Mining Algorithm

Process mining based on parallel recombination simulated annealing algorithm

2009 ◽  
Vol 29 (5) ◽  
pp. 1389-1392
Author(s):  
Ju-hong TIE ◽  
Hui PENG ◽  
Jian-hua ADU
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Process Mining ◽  
Annealing Algorithm

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
Sign in / Sign up

Export Citation Format

Share Document