Data Mining in Inductive Databases

2006 ◽  
pp. 1-23 ◽  
Author(s):  
Arno Siebes
Keyword(s):  
Data Mining ◽  
Inductive Databases

Data Mining and Knowledge Discovery in Databases

2019 ◽  
pp. 502-514 ◽  
Author(s):  
Ana Azevedo
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Great Relevance ◽  
The Subject ◽  
Inductive Databases ◽  
High Level

The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases. Within the context of inductive databases, a great relevance is given to the so-called DM languages. This chapter explores DM in KDD.

scholarly journals An XML-Based Database for Knowledge Discovery

2009 ◽  
pp. 1320-1343
Author(s):  
Rosa Meo ◽  
Giuseppe Psaila
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
General Purpose ◽  
Xml Schema ◽  
Integrity Constraints ◽  
Data Item ◽  
Source Data ◽  
Inductive Databases ◽  
Unique Framework ◽  
Basic Features

Inductive databases have been proposed as general purpose databases to support the KDD process. Unfortunately, the heterogeneity of the discovered patterns and of the different conceptual tools used to extract them from source data make the integration in a unique framework difficult. In this chapter, we explore the feasibility of using XML as the unifying framework for inductive databases, and propose a new model, XML for data mining (XDM). We show the basic features of the model, based on the concepts of data item (source data and patterns) and statement (used to manage data and derive patterns). We make use of XML namespaces (to allow the effective coexistence and extensibility of data mining operators) and of XML-schema, by means of which we can define the schema, the state and the integrity constraints of an inductive database.

scholarly journals An XML-Based Database for Knowledge Discovery

2011 ◽  
pp. 61-93
Author(s):  
Rosa Meo ◽  
Giuseppe Psaila
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
General Purpose ◽  
Integrity Constraints ◽  
Data Item ◽  
New Model ◽  
Source Data ◽  
Inductive Databases ◽  
Unique Framework ◽  
Basic Features

Inductive databases have been proposed as general purpose databases to support the KDD process. Unfortunately, the heterogeneity of the discovered patterns and of the different conceptual tools used to extract them from source data make the integration in a unique framework difficult. In this chapter, we explore the feasibility of using XML as the unifying framework for inductive databases, and propose a new model, XML for data mining (XDM). We show the basic features of the model, based on the concepts of data item (source data and patterns) and statement (used to manage data and derive patterns). We make use of XML namespaces (to allow the effective coexistence and extensibility of data mining operators) and of XMLschema, by means of which we can define the schema, the state and the integrity constraints of an inductive database.

scholarly journals An XML-Based Database for Knowledge Discovery

2011 ◽  
pp. 352-368
Author(s):  
Rosa Meo ◽  
Giuseppe Psaila
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
General Purpose ◽  
Xml Schema ◽  
Integrity Constraints ◽  
Data Item ◽  
Source Data ◽  
Inductive Databases ◽  
Unique Framework ◽  
Basic Features

Inductive databases have been proposed as general purpose databases to support the KDD process. Unfortunately, the heterogeneity of the discovered patterns and of the different conceptual tools used to extract them from source data make the integration in a unique framework difficult. In this chapter, we explore the feasibility of using XML as the unifying framework for inductive databases, and propose a new model, XML for data mining (XDM). We show the basic features of the model, based on the concepts of data item (source data and patterns) and statement (used to manage data and derive patterns). We make use of XML namespaces (to allow the effective coexistence and extensibility of data mining operators) and of XML-schema, by means of which we can define the schema, the state and the integrity constraints of an inductive database.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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