Visual and analytical mining of transactions data for production planning for production planning and marketing
| dc.contributor.author | Gurdal Ertek | |
| dc.contributor.author | Can Kuruca | |
| dc.contributor.author | Cenk Aydin | |
| dc.contributor.author | Besim Ferit Erel | |
| dc.date.accessioned | 2018-04-02T08:47:05Z | |
| dc.date.accessioned | 2023-08-20T10:56:35Z | |
| dc.date.available | 2018-04-02T08:47:05Z | |
| dc.date.available | 2023-08-20T10:56:35Z | |
| dc.date.issued | 2004-01 | |
| dc.description | Widespread use of information technology has resulted in massive collections of data regarding most aspects of an enterprise. The amount of data on the marketing side has exploded due to widespread usage of barcode systems, accounting and Enterprise Resorce Planning (ERP) software and also due to collection of Business-to-Consumer (B2C) and Business-to-Business (B2B) electronic commerce data. The amount of data that comes from manufacturing processes has also exploded, due to application of Computer Integrated Manufacturing (CIM) systems, barcode and radio frequency technology, which provide bulky amounts of real time data. | en_US |
| dc.description.abstract | Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist of filtering, clustering and comparison steps implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework | en_US |
| dc.identifier.citation | Ertek, G., Kuruca, C., Aydin, C., & Erel, B. F. (2004). Visual and analytical mining of transactions data for production planning for production planning and marketing. | en |
| dc.identifier.uri | https://edms.wexl.in/handle/1/959 | |
| dc.language.iso | en | en_US |
| dc.publisher | The Pennsylvania State University | en_US |
| dc.subject | Decision Support Systems | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Information Visualization | en_US |
| dc.title | Visual and analytical mining of transactions data for production planning for production planning and marketing | en_US |
| dc.type | Article | en_US |