Semi-automated Content Zoning of Spam Emails

in Lecture Notes on Business Information Processing. Web Information Systems and Technologies (2007)

AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies

in Abstract book of 6th International Conference on Recent Advances in Soft Computing (RASC 2006) (2006)

SANA - Security Analysis in Internet Traffic through Artificial Immune Systems

in Technical Documentary Report. United States. Air Force. Systems Command. Electronic Systems Division (2006)

A new queueing strategy for the Adversarial Queueing Theory

in IPSI-2005 (2005)

An incremental neural-based method to discover temporal skeletons in transactional data streams

in Abstract book of 5th Recent Advances in Soft Computing (RASC 2004) (2004)

An SQL-like interface to retrieve associative patterns from neural skeletons

in Proceedings 2004 International Conference on Advances in Intelligent Systems - Theory and Applications (2004)

Incremental Discovery of Association Rules with Dynamic Neural Cells

in Workshop on Symbolic Networks. ECAI 2004 (2004)

Applying Mining with Scoring

in Data Mining III, 6 (2002)

“Scoring”, in general, is defined as the usage of mining models - based on historical data - for classification or segmentation of new items. For example: if the historical data consist of classified ... [more ▼]

“Scoring”, in general, is defined as the usage of mining models - based on historical data - for classification or segmentation of new items. For example: if the historical data consist of classified customers, then we can use the model for the prediction of the behaviour of a new customer. Scoring offers novel ways to exploit the power of data mining models in everyday business activities, and proliferate mining applications to users who are not educated in mining. In this paper, we present a) the generic scoring process b) its technical mplementation, and c) an example of how scoring can be integrated in a real application. The generic process consists of three steps: The mining models are learned first, then they are transferred into the application database, and finally the models are applied to the data loaded in that database. Arguments for the necessity of such a mining improvement are collected. IBM DB2 Intelligent Miner Scoring (IM Scoring) is the first technical implementation of scoring. It is based on the emerging open-standard for mining models (Predictive Model Markup Language - PMML), and the mining extensions for SQL. Implementation issues are discussed, as well as problems that come along with its integration into operational applications. The article closes with the description of a sample application, the integration of scoring into a call center environment. A discussion of the scoring method concludes this article. [less ▲]

Mining your own Business Vol. 3 - Telecommunication

Book published by IBM Press (2001)

Mining your own Business Vol. 4 - Health Care

Book published by IBM Press (2001)