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Relevant bibliographies by topics / KNOWLEDGE DISCOVERY BASED TECHNIQUE

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Academic literature on the topic 'KNOWLEDGE DISCOVERY BASED TECHNIQUE'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"

1

Chen, Po-Chi, Ru-Fang Hsueh, and Shu-Yuen Hwang. "An ILP Based Knowledge Discovery System." International Journal on Artificial Intelligence Tools 06, no. 01 (1997): 63–95. http://dx.doi.org/10.1142/s0218213097000050.

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Interest in research into knowledge discovery in databases (KDD) has been growing continuously because of the rapid increase in the amount of information embedded in real-world data. Several systems have been proposed for studying the KDD process. One main task in a KDD system is to learn important and user-interesting knowledge from a set of collected data. Most proposed systems use simple machine learning methods to learn the pattern. This may result in efficient performance but the discovery quality is less useful. In this paper, we propose a method to integrated a new and complicated machi

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JONYER, ISTVAN, LAWRENCE B. HOLDER, and DIANE J. COOK. "GRAPH-BASED HIERARCHICAL CONCEPTUAL CLUSTERING." International Journal on Artificial Intelligence Tools 10, no. 01n02 (2001): 107–35. http://dx.doi.org/10.1142/s0218213001000441.

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Hierarchical conceptual clustering has proven to be a useful, although greatly under-explored data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides the advantages of both approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains, as well as compare SUBDUE to earlier cluste

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Weng, Cheng-Hsiung. "Knowledge discovery of digital library subscription by RFC itemsets." Electronic Library 34, no. 5 (2016): 772–88. http://dx.doi.org/10.1108/el-06-2015-0086.

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Purpose The paper aims to understand the book subscription characteristics of the students at each college and help the library administrators to conduct efficient library management plans for books in the library. Unlike the traditional association rule mining (ARM) techniques which mine patterns from a single data set, this paper proposes a model, recency-frequency-college (RFC) model, to analyse book subscription characteristics of library users and then discovers interesting association rules from equivalence-class RFC segments. Design/methodology/approach A framework which integrates the

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4

Zhang, Guan Zhu, and Yu Ye Zhu. "Research of After-Sales Management System of Enterprises Based on J2EE and Data Mining Technology." Applied Mechanics and Materials 608-609 (October 2014): 375–81. http://dx.doi.org/10.4028/www.scientific.net/amm.608-609.375.

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With the globalization of market and economy, more and more enterprises realize the importance of after-sales service system. However, traditonal after-sales service system only focuses on the business process of system, and ignores important information of after-sales service data. It is data mining technique that solves the problem as a knowledge discovery technique. Data mining technique only can discover potential and valuable information and knowledge in lots of data for decision support. The paper analyzes the business process of after-sales service of enterprises, uses the idea of J2EE

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Giustolisi, Orazio, and Dragan A. Savic. "A symbolic data-driven technique based on evolutionary polynomial regression." Journal of Hydroinformatics 8, no. 3 (2006): 207–22. http://dx.doi.org/10.2166/hydro.2006.020b.

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This paper describes a new hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming symbolic regression technique. The key idea is to employ an evolutionary computing methodology to search for a model of the system/process being modelled and to employ parameter estimation to obtain constants using least squares. The new technique, termed Evolutionary Polynomial Regression (EPR) overcomes shortcomings in the GP process, such as computational performance; number of evolutionary parameters to tune and complexity of the s

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Guan, Qing, and Jian He Guan. "Knowledge Acquisition of Interval Set-Valued Based on Granular Computing." Applied Mechanics and Materials 543-547 (March 2014): 2017–23. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2017.

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The technique of a new extension of fuzzy rough theory using partition of interval set-valued is proposed for granular computing during knowledge discovery in this paper. The natural intervals of attribute values in decision system to be transformed into multiple sub-interval of [0,1]are given by normalization. And some characteristics of interval set-valued of decision systems in fuzzy rough set theory are discussed. The correctness and effectiveness of the approach are shown in experiments. The approach presented in this paper can also be used as a data preprocessing step for other symbolic

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Li, Jian, and Jun Deng. "A Theoretical Study on Knowledge Discovery Technique for Structural Health Monitoring." Applied Mechanics and Materials 166-169 (May 2012): 1250–53. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.1250.

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Based on the similarity between knowledge discovery from data bases (KDD) and Structural health monitoring (SHM), and considered the particularity of SHM problems, a four-step framework of SHM is proposed. The framework extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is consisted by second order structural parameter identification as feature extraction and statistical control chart analys

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Usai, Antonio, Marco Pironti, Monika Mital, and Chiraz Aouina Mejri. "Knowledge discovery out of text data: a systematic review via text mining." Journal of Knowledge Management 22, no. 7 (2018): 1471–88. http://dx.doi.org/10.1108/jkm-11-2017-0517.

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Purpose The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new

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Mahoto, Naeem Ahmed, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon, and Adel Sulaiman. "Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment." Sustainability 13, no. 16 (2021): 8900. http://dx.doi.org/10.3390/su13168900.

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The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transform

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10

UYSAL, İLHAN, and H. ALTAY GÜVENIR. "An overview of regression techniques for knowledge discovery." Knowledge Engineering Review 14, no. 4 (1999): 319–40. http://dx.doi.org/10.1017/s026988899900404x.

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Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive parti

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