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OLAP Mining: An Integration of OLAP with Data Mining

TLDR
OLAP mining is a mechanism which integrates on-line analytical processing with data mining so that mining can be performed in different portions of databases or data warehouses and at different levels of abstraction at user’s finger tips.
Abstract
OLAP mining is a mechanism which integrates on-line analytical processing (OLAP) with data mining so that mining can be performed in different portions of databases or data warehouses and at different levels of abstraction at user’s finger tips. With rapid developments of data warehouse and OLAP technologies in database industry, it is promising to develop OLAP mining mechanisms.

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Citations
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Patent

Attributes of captured objects in a capture system

TL;DR: In this paper, a system and method for capturing objects and balancing systems resources in a capture system is described, where an object is captured, metadata associated with the object is generated, and the object and metadata stored.
Patent

File system for a capture system

TL;DR: In this article, a file system is provided in a capture system to efficiently read and write captured objects, which includes a plurality of queues to queue captured objects to be written to a disk and a disk controller configured to write contiguous blocks of data from the selected queue to the selected partition.
Journal ArticleDOI

Towards on-line analytical mining in large databases

TL;DR: In this article, a data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses, including characterization, comparison, association, classification, prediction, and clustering.
Journal ArticleDOI

Database technology for decision support systems

TL;DR: More work must be done to develop domain-independent tools that solve the data cleaning problems associated with data warehouse development, and to achieve better synergy between database systems and data mining technology.
Patent

System and method for data mining and security policy management

TL;DR: In this article, the authors present a method for generating a query for a database for information stored in the database and then generating an Online Analytical Processing (OLAP) element to represent information received from the query.
References
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Book

C4.5: Programs for Machine Learning

TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Proceedings Article

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article

Fast algorithms for mining association rules

TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.