Recently, inductive databases (IDBs) have been proposed to tackle the problem of
knowledge discovery from huge databases. With an IDB, the user/analyst performs a set of …

With the widespread computerization in business, government, and science, the efficient
and effective discovery of interesting information from large databases becomes essential …

Classification is a data mining (DM) technique that generates classes allowing to predict and
describe the behavior of a variable based on the characteristics of a dataset. Frequently, DM …

There has been much recent interest in on-line data mining. Existing mining algorithms
designed for stored data are either not applicable or not effective on data streams, where …

Most of today's techniques for data mining and association rule mining (ARM) in particular,
are really “flat file mining”, since the database is typically dumped to an intermediate flat file …

Many efforts have been devoted to couple data mining activities with relational DBMSs, but a
true integration into the relational DBMS kernel has been rarely achieved. This paper …

Nowadays the data collected in the process in maintenance systems comprise a big portion
of the related databases. Analyzing these maintenance data provides the firms, enterprises …

Managing large collections of data mining models Page 1 MANAGING LARGE
COLLECTIONS OF DATA MINING MODELS By Bing Liu and Alexander Tuzhilin Model …

Many organizations today have more than very large databases; they have databases that
grow without limit at a rate of several million records per day. Mining these continuous data …

Almost a decade ago, Imielinski and Mannila introduced the notion of Inductive Databases
to manage KDD applications just as DBMSs successfully manage business applications …