Modeling KDD Processes within the Inductive Database Framework

Abstract

Abstract. One of the most challenging problems in data manipulation in the future is to be able to e ciently handle very large databases but also multiple induced properties or generalizations in that data. Popular examples of useful properties are association rules, and inclusion and functional dependencies. Our view of a possible approach for this task is to specify and query inductive databases, which are databases that in addition to data also contain intensionally de ned generalizations about the data. We formalize this concept and show h o w i t c a n be used throughout the whole process of data mining due to the closure property of the framework. We show that simple query languages can be de ned using normal database terminology. W e demonstrate the use of this framework to model typical data mining processes. It is then possible to perform various tasks on these descriptions like, e.g., optimizing the selection of interesting properties or comparing two processes.

A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data

“…Starting from raw gene expression data, it supports the analysis and the discovery of relevant biological information via a constraint-based bi-set mining approach from computed boolean data sets. The generic process is described within the framework of inductive databases, i.e., each step of the process can be formalized as a query on data and/or patterns that satisfy some constraints [12,13]. It leads us to a formalization of boolean gene expression data enrichment.…”

Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining

“…In the cInQ 1 project, we want to develop a new generation of databases, called "inductive databases" (IDBs), suggested by Imielinski and Mannila in [15] and formalized in, e.g., [10]. This kind of databases integrate raw data with knowledge extracted from raw data, materialized under the form of patterns into a common framework that supports the knowledge discovery process within a database framework.…”

On the Use of Information Systems Research Methods in Datamining

“…In Boulicaut et al (1999) an inductive databases framework for the data mining and knowledge discovery in databases (KDD) modeling was introduced. The basic idea here is that data-mining task can be formulated as locating interesting sentences from a given logic that are true in the database.…”

“…Then discovering knowledge from data can be viewed as querying the set of interesting sentences. Therefore the term "an inductive database" refers to such a type of databases that contains not only the data but a theory about the data as well (Boulicaut et al, 1999).…”

“…Certainly, there might be a need to find a solution about what should be presented to a user and when to stop the recursive rule generation while querying. We refer an interested reader to the work of Boulicaut et al (1999).…”