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Study on knowledge discovery in traditional Chinese medical case records  [cached]
You-hua WANG,Pei-yong ZHENG
Zhong Xi Yi Jie He Xue Bao , 2007,
Abstract: ABSTRACT: Traditional Chinese medical case records in the previous dynasties are vital to the development of traditional Chinese medical theory, but the tremendous amount of data are far beyond a person's ability for comprehension. According to information science, traditional Chinese medical case record data are complicated and intricate experiential data. New technology and methods are needed to solve this difficulty. Knowledge discovery technology plays an important role in analyzing data and uncovering important data patterns, and it will be a useful method in processing such data.This paper briefly presents the methods of knowledge discovery in traditional Chinese medical case record study,and puts forward some necessary academic methods.
A Case Study in Knowledge Discovery and Elicitation in an Intelligent Tutoring Application  [PDF]
Ann Nicholson,Tal Boneh,Tim Wilkin,Kaye Stacey,Liz Sonenberg,Vicki Steinle
Computer Science , 2013,
Abstract: Most successful Bayesian network (BN) applications to datehave been built through knowledge elicitation from experts.This is difficult and time consuming, which has lead to recentinterest in automated methods for learning BNs from data. We present a case study in the construction of a BN in anintelligent tutoring application, specifically decimal misconceptions. Wedescribe the BN construction using expert elicitation and then investigate how certainexisting automated knowledge discovery methods might support the BN knowledge engineering process.
A Study of Data Mining Tools in Knowledge Discovery Process  [PDF]
Y. Ramamohan,K. Vasantharao,C. Kalyana Chakravarti,A.S.K.Ratnam
International Journal of Soft Computing & Engineering , 2012,
Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. It uses machine learning, statistical and visualization techniques to discovery and present knowledge in a form which is easily comprehensible to humans. Various popular data mining tools are available today. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. This paper presents an overview of the data mining tools like Weka, Tanagra, Rapid Miner, Orange.
BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data  [PDF]
Feichen Shen, Yugyung Lee
Journal of Intelligent Learning Systems and Applications (JILSA) , 2018, DOI: 10.4236/jilsa.2018.101001
A large number of ontologies have been introduced by the biomedical community in recent years. Knowledge discovery for entity identification from ontology has become an important research area, and it is always interesting to discovery how associations are established to connect concepts in a single ontology or across multiple ontologies. However, due to the exponential growth of biomedical big data and their complicated associations, it becomes very challenging to detect key associations among entities in an inefficient dynamic manner. Therefore, there exists a gap between the increasing needs for association detection and large volume of biomedical ontologies. In this paper, to bridge this gap, we presented a knowledge discovery framework, the BioBroker, for grouping entities to facilitate the process of biomedical knowledge discovery in an intelligent way. Specifically, we developed an innovative knowledge discovery algorithm that combines a graph clustering method and an indexing technique to discovery knowledge patterns over a set of interlinked data sources in an efficient way. We have demonstrated capabilities of the BioBroker for query execution with a use case study on a subset of the Bio2RDF life science linked data.
A process for mining science & technology documents databases, illustrated for the case of "knowledge discovery and data mining"
Zhu, Donghua;Porter, Alan;Cunningham, Scott;Carlisie, Judith;Nayak, Anustup;
Ciência da Informa??o , 1999, DOI: 10.1590/S0100-19651999000100002
Abstract: this paper presents a process of mining research & development abstract databases to profile current status and to project potential developments for target technologies, the process is called "technology opportunities analysis." this article steps through the process using a sample data set of abstracts from the inspec database on the topic o "knowledge discovery and data mining." the paper offers a set of specific indicators suitable for mining such databases to understand innovation prospects. in illustrating the uses of such indicators, it offers some insights into the status of knowledge discovery research*.
Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients  [PDF]
Aboul ella Hassanien,Mohamed E. Abdelhafez,Hala S. Own
Advances in Fuzzy Systems , 2008, DOI: 10.1155/2008/528461
Abstract: The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.
Gest o do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras Knowledge management using data mining: a case study of the Federal University of Lavras
Olinda Nogueira Paes Cardoso,Rosa Teresa Moreira Machado
Revista de Administra??o Pública , 2008, DOI: 10.1590/s0034-76122008000300004
Abstract: A gest o do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utiliza o de tecnologias de informa o para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produ o científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limita es encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribui o foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elabora o de novas políticas de gest o científica e tecnológica e aperfei oamento do sistema de ensino superior brasileiro. The management of knowledge embraces every form of production, storage, distribution and use of the knowledge, making necessary the use of information technologies to facilitate the process, due to the great increase in the volume of data. An emergent methodology that tries to solve the problem of the analysis of great amounts of data is the knowledge discovery in database (KDD) and data mining, a technique that is part of this methodology. This article aims to develop, apply and analyze a tool of data mining, to extract knowledge regarding people's scientific production involved with the research at the Federal University of Lavras (Ufla). The methodology used involved bibliographical research, documental research, and method of case study. Once it was just used referring data to the scientific production of Ufla. The limitations found in the analysis of the results indicate that it is still necessary to standardize the completion of the Lattes curricula to refine the analyses, and establish indicators. The result was the creation of a structured database, which is part of a larger process of development of science and technology indicators, with the objective of aiding the elaboration of new policies of scientific and technological management and improvement of the superior education system in Brazil.
A Comparative Study to Select a Soft Computing Model for Knowledge Discovery in Data Mining
Dharmpal Singh
International Journal of Artificial Intelligence & Knowledge Discovery , 2012,
Abstract: The ABC algorithm imitates the behaviors of real bees in finding food sources and sharing the information with other bees. The said algorithm works based on three categories of bees, that is, employed, onlooker and scout bees that communicate with each other in sharing the information of the food sources Since ABC algorithm is simple in concept, easy to implement, and has fewer control parameters, it has been widely used in many fields. This paper concerns primarily about how to use artificial bee colony to extract the knowledge from the data mining on the Boston city data .In this paper, we have formed the association rule of data by PCA and then used other soft computing model like Artificial Neural Network, PSO and ABC algorithm along with fuzzy logic. After that, selected the optimal model based on the minimum error and that will be used to discovered the knowledge in data.
Universal Knowledge Discovery from Big Data: Towards a Paradigm Shift from 'Knowledge Discovery' to 'Wisdom Discovery'  [PDF]
Bin Shen
Computer Science , 2014,
Abstract: Many people hold a vision that big data will provide big insights and have a big impact in the future, and big-data-assisted scientific discovery is seen as an emerging and promising scientific paradigm. However, how to turn big data into deep insights with tremendous value still remains obscure. To meet the challenge, universal knowledge discovery from big data (UKD) is proposed. The new concept focuses on discovering universal knowledge, which exists in the statistical analyses of big data and provides valuable insights into big data. Universal knowledge comes in different forms, e.g., universal patterns, rules, correlations, models and mechanisms. To accelerate big data assisted universal knowledge discovery, a unified research paradigm should be built based on techniques and paradigms from related research domains, especially big data mining and complex systems science. Therefore, I propose an iBEST@SEE methodology. This study lays a solid foundation for the future development of universal knowledge discovery, and offers a pathway to the discovery of "treasure-trove" hidden in big data.
Data Mining, Applications and Knowledge Discovery
Ms. Neha Purohit , Ms. Sapna Purohit , Mr. Ritesh Kumar Purohit
International Journal of Advanced Computer Research , 2012,
Abstract: This paper explores about the mining of data and finding essential information from huge amounts of data. Extracting or “mining” knowledge from large amounts of data is known as Data Mining. Use of algorithms to extract the information and patterns is derived by the KDD process. Knowledge Discovery in Databases is a process of finding useful information and patterns in data. Research in data mining continues growing in business and in learning organization over coming decades.
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