The Role of Granular Information in Knowledge Discovery in Databases

2005 ◽  
pp. 275-305
Author(s):  
Witold Pedrycz
Keyword(s):  
Knowledge Discovery ◽  
Knowledge Discovery In Databases

Knowledge Creation in Inquiring Organizations using KDD

2011 ◽  
pp. 133-153
Author(s):  
John D. Murray ◽  
Thomas L. Case ◽  
Adrian B. Gardiner
Keyword(s):  
Data Mining ◽  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Knowledge Creation ◽  
Knowledge Discovery In Databases ◽  
Future Research ◽  
Information Foraging ◽  
Inquiring Systems ◽  
Concept Of Information

Churchman (1971) emphasized the continual learning nature of organizations as part of their ontological fabric. Accordingly, he proffered the view of organizations as inquiring systems whose actions result in the creation of knowledge. To this end, many modern organizations have attempted to create knowledge by using technologies, such as Data Mining and Knowledge Discovery in Databases (KDD). Although quite powerful, these technologies depend heavily upon the skill and insights of the analyst. We propose that the role of the analyst in the application of these technologies is poorly understood. To advance our understanding in this regard, we dedicate the first part of this chapter to describing the KDD process and relate it to the five philosophical perspectives of organizational knowledge acquisition, as originally discussed by Churchman (1971). In the second part of the chapter, we draw parallels between the process of knowledge acquisition via KDD with the concept of information foraging (Pirolli & Card, 1999). Information foraging theory is offered as a research lens through which we can investigate the role of human judgment in KDD. These insights lead us to propose a number of areas for possible future research. Based on our insights into information foraging and knowledge creation, the chapter concludes by introducing a new organizational metaphor into corporate epistemology: inquiring organizations as knowledge foragers.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

scholarly journals Proposta de metodologia para a criação de etiqueta de classificação – estudo de caso: desempenho escolar

2016 ◽  
Vol 23 (1) ◽  
pp. 177-191
Author(s):  
Anderson Roges Teixeira Góes ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Vector Machines

Resumo A qualidade na educação tem sido objeto de muita discussão, seja nas escolas e entre seus gestores, seja na mídia ou na literatura. No entanto, uma análise mais profunda na literatura parece não indicar técnicas que explorem bancos de dados com a finalidade de obter classificações para o desempenho escolar, nem tampouco há um consenso sobre o que seja “qualidade educacional”. Diante deste contexto, neste artigo, é proposta uma metodologia que se enquadra no processo KDD (Knowledge Discovery in Databases, ou seja, Descoberta de Conhecimento em Bases de Dados) para a classificação do desempenho de instituições de ensino, de forma comparativa, com base nas notas obtidas na Prova Brasil, um dos itens integrantes do Índice de Desenvolvimento da Educação Básica (IDEB) no Brasil. Para ilustrar a metodologia, esta foi aplicada às escolas públicas municipais de Araucária, PR, região metropolitana de Curitiba, PR, num total de 17, que, por ocasião da pesquisa, ofertavam Ensino Fundamental, considerando as notas obtidas pela totalidade dos alunos dos anos iniciais (1º. ao 5º. ano do ensino fundamental) e dos anos finais (6º. ao 9º. ano do ensino fundamental). Na etapa de Data Mining, principal etapa do processo KDD, foram utilizadas três técnicas de forma comparativa para o Reconhecimento de Padrões: Redes Neurais Artificiais; Support Vector Machines; e Algoritmos Genéticos. Essas técnicas apresentaram resultados satisfatórios na classificação das escolas, representados por meio de uma “Etiqueta de Classificação do Desempenho”. Por meio desta etiqueta, os gestores educacionais poderão ter melhor base para definir as medidas a serem adotadas junto a cada escola, podendo definir mais claramente as metas a serem cumpridas.

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