scholarly journals Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

2019 ◽  
pp. 3-16 ◽  
Author(s):  
Miguel Couceiro ◽  
Amedeo Napoli
Keyword(s):  
Knowledge Discovery ◽  
Knowledge Based

Expression and Processing of Inductive Queries

2009 ◽  
pp. 518-526
Author(s):  
Edgard Benítez-Guerrero ◽  
Omar Nieva-García
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Pattern Discovery ◽  
Data Preprocessing ◽  
Knowledge Discovery In Databases ◽  
Digital Information ◽  
Interactive Process ◽  
Useful Knowledge ◽  
Knowledge Based ◽  
Computer Based

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

Data Mining and Knowledge Discovery

2010 ◽  
pp. 84-110
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Intelligent Manufacturing ◽  
Support Vector ◽  
Knowledge Based ◽  
Database Technology ◽  
Multi Agent ◽  
Basic Functions ◽  
Visualization Technology ◽  
Clustering Data

In Chapters 2 and 3, the knowledge-based system and Multi-Agent system were illustrated. These are significant methods and theories of Manufacturing Intelligence (MI). Data Mining (DM) and Knowledge Discovery (KD) are at the foundation of MI. Humans are immersed in data, but are thirsty for knowledge. With the wider application of database technology, a dilemma has arisen whereby people are ‘rich in data, poor in knowledge’. The explosion of knowledge and information has brought great benefit to mankind, but has also carried with it certain drawbacks, since it has resulted in knowledge and information ‘pollution. Facing a vast but polluted ocean of data, a technical means to discard the bad and retain the good was sought. Data Mining and Knowledge Discovery (DMKD) was therefore proposed against the background of rapidly expanding data and databases. It is also the result of the development and fusion of database technology, Artificial Intelligence (AI), statistical techniques and visualization technology (Fayyad U., 1998). DMKD has become a research focus and cutting-edge technology in the field of computer information processing (Jef Woksem, 2001). The development background, conception, working process, classification and general application of DM and KD are firstly introduced in this chapter. Secondly, basic functions and assignment such as prediction, description, data clustering, data classification, conception description and visualization processing are discussed. Then the methods and tools for DM are presented, such as the association rule, decision tree, genetic algorithm, rough set and support vector machine. Finally, the application of DMKD in intelligent manufacturing is summarized.

Building Data Mining Models in the Oracle 9i Environment

10.28945/2697 ◽  
2003 ◽  
Author(s):  
Krzysztof Hauke ◽  
Mievzyslaw L. Owoc ◽  
Maciej Pondel
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Business Intelligence ◽  
Knowledge Bases ◽  
Crucial Issue ◽  
Knowledge Based ◽  
Whole Process ◽  
Database Server ◽  
Testing Data ◽  
Verification Techniques

Data Mining (DM) is a very crucial issue in knowledge discovery processes. The basic facilities to create data mining models were implemented successfully on Oracle 9i as the extension of the database server. DM tools enable developers to create Business Intelligence (BI) applications. As a result Data Mining models can be used as support of knowledge-based management. The main goal of the paper is to present new features of the Oracle platform in building and testing DM models. Authors characterize methods of building and testing Data Mining models available on the Oracle 9i platform, stressing the critical steps of the whole process and presenting examples of practical usage of DM models. Verification techniques of the generated knowledge bases are discussed in the mentioned environment.

Cooperative Knowledge Evolution: A Construction-Integration Approach to Knowledge Discovery in Medicine

1998 ◽  
Vol 37 (04/05) ◽  
pp. 491-500
Author(s):  
B. Tschaitschian ◽  
F. J. Schmalhofer
Keyword(s):  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Integration Theory ◽  
Knowledge Evolution ◽  
Knowledge Based ◽  
Construction Integration ◽  
Acquisition Processes ◽  
Cooperative Knowledge ◽  
Analysis Of Knowledge ◽  
Evolution Systems

AbstractIn this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.

Knowledge Discovery Solutions for Intelligent Enterprises

2005 ◽  
pp. 1747-1751
Author(s):  
Nilmini Wickramasinghe ◽  
Sushil K. Sharma
Keyword(s):  
Knowledge Discovery ◽  
High Performance ◽  
New Technologies ◽  
Customer Relationship ◽  
Business Strategies ◽  
Technology Knowledge ◽  
Demand Planning ◽  
Knowledge Based ◽  
Knowledge Based Economy ◽  
New Knowledge

The exponential increase in information—primarily due to the electronic capture of data and its storage in vast data warehouses—has created a demand for analyzing the vast amount of data generated by today’s organizations so that enterprises can respond quickly to fast changing markets. These applications not only involve the analysis of the data but also require sophisticated tools for analysis. Knowledge discovery technologies are the new technologies that help to analyze data and find relationships from data to finding reasons behind observable patterns. Such new discoveries can have profound impact on designing business strategies. With the massive increase in data being collected and the demands of a new breed of intelligent applications like customer relationship management, demand planning and predictive forecasting, the knowledge discovery technologies have become necessities to providing high performance and feature rich intelligent application servers for intelligent enterprises. The new knowledge based economy entirely depends upon information technology, knowledge sharing, as well as intellectual capital and knowledge management.

A Model for Acquisition of Implicit Design Knowledge Based on KDD

2006 ◽  
Vol 505-507 ◽  
pp. 505-510
Author(s):  
Jing Min Li ◽  
Jin Yao ◽  
Yong Mou Liu
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Product Design ◽  
Domain Knowledge ◽  
Design System ◽  
Design Knowledge ◽  
Knowledge Based ◽  
Knowledge Discovery In Database ◽  
Knowledge Intensive ◽  
Set Up

Knowledge discovery in database (KDD) represents a new direction of data processing and knowledge innovation. Design is a knowledge-intensive process driven by various design objectives. Implicit knowledge acquisition is key and difficult for the intelligent design system applied to mechanical product design. In this study, the characteristic of implicit design knowledge and KDD are analyzed, a model for product design knowledge acquisition is set up, and the key techniques including the expression and application of domain knowledge and the methods of knowledge discovery are discussed. It is illustrated by an example that the method proposed can be used to obtain the engineering knowledge in design case effectively, and can promote the quality and intelligent standard of product design.

On Knowledge Based Intelligent Systems

2013 ◽  
Vol 448-453 ◽  
pp. 3557-3560
Author(s):  
Li Xia Song ◽  
Yi Hui Zhang
Keyword(s):  
Knowledge Discovery ◽  
Intelligent Systems ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Automatic Acquisition

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advanceof the technologies involved.

Is the Personal Always Political?

2017 ◽  
Vol 38 (3) ◽  
pp. 133-143 ◽  
Author(s):  
Danny Osborne ◽  
Yannick Dufresne ◽  
Gregory Eady ◽  
Jennifer Lees-Marshment ◽  
Cliff van der Linden
Keyword(s):  
Survey Data ◽  
National Survey ◽  
Negative Relationship ◽  
Openness To Experience ◽  
Political Sophistication ◽  
Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

Connectives Increase Knowledge-Based Inferences

1994 ◽  
Author(s):  
Gregory Barker ◽  
Keith Millis ◽  
Jonathan M. Golding
Keyword(s):  
Knowledge Based
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