Expanding application demand for data mining of massive data warehouses has fueled advances in automated predictive methods. We examine a few successful application areas and their technical challenges. We review the key theoretical developments in PAC and statistical learning theory that have lead to the development of support vector machines and to the use of multiple models for increased pre...

1 ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage an...

The objective of this paper is to present cost models for estimating the response time for the distributed data mining (DDM) process. These cost models form the basis for developing a hybrid approach to distributed data mining which integrates the client-server and mobile agent paradigms. The underlying objective of the hybrid model is to optimise the DDM process.

Only for you today! Discover your favourite privacy preserving data mining models and algorithms book right here by downloading and getting the soft file of the book. This is not your time to traditionally go to the book stores to buy a book. Here, varieties of book collections are available to download. One of them is this privacy preserving data mining models and algorithms as your preferred ...

This thesis documents the design, implementation and test of Probabilistic Relational Models (PRMs). PRMs are a graphical statistical approach to modeling relational data using the Relational Language. PRMs consist of two components; the dependency structure and the parameters. Our design is based on simplicity, flexibility , and performance. We explain the search over possible structures, usin...

Huge financial databases may contain terabytes of data and have truly industrial dimensions. Since the quality of conclusions drawn using the data depends primarily on the information quality, the data has to be to monitored using appropriate methods. This paper discusses statistical model-based methods, including process monitoring approaches, applied with respect to the aggregated data, as we...

Data mining has various applications for customer relationship management. In this proposal, we are introducing a framework for identifying appropriate data mining techniques for various CRM activities. This Research attempts to integrate the data mining and CRM models and to propose a new model of Data mining for CRM. The new model specifies which types of data mining processes are suitable fo...

In recent years, process mining emerged as a new and exciting collection of analysis approaches. Process mining combines process models and event data in various novel ways. As a result, one can find out what people and organizations really do. For example, process models can be automatically discovered from event data. Compliance can be checked by confronting models with event data. Bottleneck...