Knowledge Discovery and Data Mining

Jan Fabian Ehmke ()
Additional contact information
Jan Fabian Ehmke: University of Braunschweig

Chapter Chapter 4 in Integration of Information and Optimization Models for Routing in City Logistics, 2012, pp 37-57 from Springer

Abstract: Abstract Nowadays, it is possible to collect detailed data about the current state and the operations of systems such as transportation systems. Due to technological advancements, we may collect and store enormous amounts of operational data at low costs. These data are usually not properly exploited, because the derivation of relevant information for the improvement of planning systems is challenging. However, planning systems rely on such information describing the typical behavior of a system, which can be derived from aggregates of operational data. Based on typical system behavior, future operations can be planned.

Keywords: Cluster Algorithm; Data Object; Cluster Approach; Information Model; Mass Data (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4614-3628-7_4

Ordering information: This item can be ordered from
http://www.springer.com/9781461436287

DOI: 10.1007/978-1-4614-3628-7_4

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().