Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. Nowadays there exist many DM algorithms, developed, implemented and available for direct use or integration into specific solution. There exist also a number of DM systems that provide DM tools for all steps of the DM process. This paper is aimed at provoking the discussio...

Semantic Web Mining can be considered as Data Mining (DM) for/from the Semantic Web. Current DM systems could serve the purpose of Semantic Web Mining if they were more compliant with, e.g., the standards of representation for ontologies and rules in the Semantic Web and/or interoperable with well-established tools for Ontological Engineering (OE) that support these standards. In this paper we ...

CRISP-DM(CRoss-Industry Standard Process for Data Mining) has its origins in the second half of nineties and is thus about two decades old. According to many surveys user polls it still de facto standard developing data mining knowledge discovery projects. However, undoubtedly field moved on considerably twenty years, with science now leading term being favoured over mining. In this paper we in...

Data Mining is a research line that began in 1980 in order to find the knowledge that is hidden in the data that organizations are storing in a daily basis. This knowledge supports the decision-making processes in organizations. As a consequence companies of every kind have been developing data mining projects since the term appeared. However, there is no way to estimate this kind of projects. ...

The state of the art in the domain of knowledge discovery in databases (KDD) and data mining (DM) has reached the point where the existence of various languages is becoming highly desirable. This paper presents an XML-based language called DMSL (Data Mining Specification Language). Its purpose is to provide the framework for platform-independent definition of the whole data mining process, and ...

Grid Computing brought the promise of making high-performance computing cheaper and more easily available than traditional supercomputing platforms. Such a promise was very well received by the data mining (DM) community, as DM applications typically process very large datasets and are thus very resource intensive. However, since the Grid is very dynamic and parallel data mining is prone to loa...

This article reviews the literature in the search for the multifaceted applications of data mining (DM), business intelligence (BI), and knowledge management (KM). The literature review highlights the overviews of DM, BI, and KM; the practical applications of DM, BI, and KM; and the prospects of DM, BI, and KM in terms of marketing, business, human resources, and manufacturing. DM plays a key r...

The aim of this chapter is to present the main statistical issues in Data mining (DM) and Knowledge Data Discovery (KDD) and to examine whether traditional statistics approach and methods substantially differ from the new trend of KDD and DM. We address and emphasize some central issues of statistics which are highly relevant to DM and have much to offer to DM.

We propose an intelligent data mining (DM) assistant that will combine planning and meta-learning to provide support to users of a virtual DM laboratory. A knowledge-driven planner will rely on a data mining ontology to plan the knowledge discovery workflow and determine the set of valid operators for each step of this workflow. A probabilistic metalearner will select the most appropriate opera...

KDD is a rapidly expanding field with promise for great applicability. Knowledge discovery became the new database technology for the incoming years. The need for automated discovery tools caused an explosion in the number and type of tools available commercially and in the public domain. These requirements encouraged us to propose a new KDD model so called ODBC_KDD(2) described in [39] ."One o...