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Academic literature on the topic 'KNOWLEDGE DISCOVERY BASED TECHNIQUE'
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Journal articles on the topic "KNOWLEDGE DISCOVERY BASED TECHNIQUE"
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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 machine learning method called inductive logic programming (ILP) to improve the KDD quality. Such integration shows how this new learning technique can be easily applied to a KDD system and how it can improve the representation of the discovery. In our system, we use user's queries to indicate the importance and interestingness of the target knowledge. The system has been implemented on a SUN workstation using the Sybase database system. Detailed examples are also provided to illustrate the benefit of integrating the ILP technique with the KDD system.
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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 clustering algorithms. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data.
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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 RFC model and ARM technique is proposed to analyse book subscription characteristics of library users. First, the author applies the RFC model to determine library users’ RFC values. After that, the author clusters library users’ transactions into several RFC segments by their RFC values. Finally, the author discovers RFC association rules and analyses book subscription characteristics of RFC segments (library users). Findings The paper provides experimental results from the survey data. It shows that the precision of the frequent itemsets discovered by the proposed RFC model outperforms the traditional approach in predicting library user subscription itemsets in the following time periods. Besides, the proposed approach can discover interesting and valuable patterns from library book circulation transactions. Research limitations/implications Because RFC thresholds were assigned based on expert opinion in this paper, it is an acquisition bottleneck. Therefore, researchers are encouraged to automatically infer the RFC thresholds from the library book circulation transactions. Practical implications The paper includes implications for the library administrators in conducting library book management plans for different library users. Originality/value This paper proposes a model, the RFC model, to analyse book subscription characteristics of library users.
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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 design mode, and expounds the development design of the system including the design of J2EE frame, functional module, system component and database.
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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 symbolic models. Similarly, it alleviates issues arising from numerical regression, including difficulties in using physical insight and over-fitting problems. This paper demonstrates that EPR is good, both in interpolating data and in scientific knowledge discovery. As an illustration, EPR is used to identify polynomial formulæ with progressively increasing levels of noise, to interpolate the Colebrook-White formula for a pipe resistance coefficient and to discover a formula for a resistance coefficient from experimental data.
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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 knowledge discovery or machine learning methods other than rough set theory.
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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 analysis of identified stiffness for feature analysis is then presented. Through clarifying the goal and hierarchy of extracting useful knowledge of SHM problems, the framework has potential to facilitate the further development of SHM.
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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 topics. Design/methodology/approach This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1). Findings The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment. Originality/value This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.
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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 transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.
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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 partitioning regression methods that induce regression trees (CART, RETIS and M5).