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Journal articles on the topic 'KNOWLEDGE DISCOVERY BASED TECHNIQUE'

Author: Grafiati

Published: 11 September 2023

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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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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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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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COBLE, JEFFREY A., RUNU RATHI, DIANE J. COOK, and LAWRENCE B. HOLDER. "ITERATIVE STRUCTURE DISCOVERY IN GRAPH-BASED DATA." International Journal on Artificial Intelligence Tools 14, no. 01n02 (2005): 101–24. http://dx.doi.org/10.1142/s0218213005002016.

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Much of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event-driven or relationally structured. In this paper we present approaches to address two related challenges; the need to assimilate incremental data updates and the need to mine monolithic datasets. M

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Adala, Asma, Nabil Tabbane, and Sami Tabbane. "A Framework for Automatic Web Service Discovery Based on Semantics and NLP Techniques." Advances in Multimedia 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/238683.

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As a greater number of Web Services are made available today, automatic discovery is recognized as an important task. To promote the automation of service discovery, different semantic languages have been created that allow describing the functionality of services in a machine interpretable form using Semantic Web technologies. The problem is that users do not have intimate knowledge about semantic Web service languages and related toolkits. In this paper, we propose a discovery framework that enables semantic Web service discovery based on keywords written in natural language. We describe a n

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Alsukhni, Emad, Ahmed AlEroud, and Ahmad A. Saifan. "A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies." International Journal of Information Technology and Web Engineering 14, no. 1 (2019): 112–31. http://dx.doi.org/10.4018/ijitwe.2019010106.

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Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible

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Thilakaratne, Menasha, Katrina Falkner, and Thushari Atapattu. "A systematic review on literature-based discovery workflow." PeerJ Computer Science 5 (November 18, 2019): e235. http://dx.doi.org/10.7717/peerj-cs.235.

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As scientific publication rates increase, knowledge acquisition and the research development process have become more complex and time-consuming. Literature-Based Discovery (LBD), supporting automated knowledge discovery, helps facilitate this process by eliciting novel knowledge by analysing existing scientific literature. This systematic review provides a comprehensive overview of the LBD workflow by answering nine research questions related to the major components of the LBD workflow (i.e., input, process, output, and evaluation). With regards to theinputcomponent, we discuss the data types

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Ramzan Begam, M., and P. Sengottuvelan. "Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses." International Journal of Engineering & Technology 7, no. 3.27 (2018): 91. http://dx.doi.org/10.14419/ijet.v7i3.27.17662.

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Day to day involvement in crime becomes higher statistics for providing information against crime occurrences. A crime committed in different locations, the point of crime occurrence, strategy be analyzed very tedious using only information records. Because information collection in the form of attribute case records with direct crime rates score, so valid factor identification of crime category is a problem. By using the crime cluster in data mining technique to analyze the criminal records to propose a sentence case relative clustering algorithm (SCRCA)with addition classification rule minin

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Bhat, Prashant, and Pradnya Malaganve. "Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database." International Journal of Intelligent Systems and Applications 13, no. 4 (2021): 38–48. http://dx.doi.org/10.5815/ijisa.2021.04.04.

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Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels

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Nata, Gusti Ngurah Mega, Steven Anthony, and Putu Pande Yudiastra. "Knowledge Discovery And Virtual Tour To Support Tourism Promotion." IAIC Transactions on Sustainable Digital Innovation (ITSDI) 2, no. 2 (2020): 94–106. http://dx.doi.org/10.34306/itsdi.v2i2.387.

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Planning a tourism trip is an important part for tourists so that their tour is satisfying. Service bureaus that have a function to help provide information and prepare tourist travel plans for tourists often provide random destination choices because they do not know the pattern of selecting tourist destinations. This will be detrimental to tourists when service bureaus make wrong tourism travel plans. Tourists also often find it difficult to determine which tourist destination to go to because they do not know the environmental conditions in tourist destinations. To overcome this problem, in

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Deng, Jun, Jian Li, and Daoyao Wang. "Knowledge Discovery from Vibration Measurements." Scientific World Journal 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/917524.

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The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering the particularity of SHM problems, a four-step framework of SHM is proposed which extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes a

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Rani, Mrs M. Akila, and Dr D. Shanthi. "A Study on Knowledge Discovery of Relevant Web Services with Semantic and Syntactic approaches." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 8–11. http://dx.doi.org/10.24297/ijct.v4i1a.3026.

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Web mining is the application of data mining techniques to discover patterns from the Web. Web services defines set of standards like WSDL(Web Service Description Language), SOAP(Simple Object Access Protocol) and UDDI(Universal Description Discovery and Integration) to support service description, discovery and invocation in a uniform interchangeable format between heterogeneous applications. Due to huge number of Web services and short content of WSDL description, the identification of correct Web services becomes a time consuming process and retrieves a vast amount of irrelevant Web service

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Preiss, Judita, Mark Stevenson, and Robert Gaizauskas. "Exploring relation types for literature-based discovery." Journal of the American Medical Informatics Association 22, no. 5 (2015): 987–92. http://dx.doi.org/10.1093/jamia/ocv002.

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Abstract Objective Literature-based discovery (LBD) aims to identify “hidden knowledge” in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques re

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Turčínek, Pavel, and Arnošt Motyčka. "Knowledge discovery on consumers’ behaviour." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2893–901. http://dx.doi.org/10.11118/actaun201361072893.

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This paper summarizes results of the research project “Application of modern methods to data processing in the field of marketing research” which was solved at the Department of Informatics, Faculty of Business and Economics of Mendel University in Brno. The most of these results were presented at international conferences.It describes the use of knowledge discovery techniques on data from marketing research of consumers’ behaviour. The paper deals with issues of classification, cluster analysis, correlation and association rules.For classification there were used various algorithms: multi-lay

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Zhang, Jia, Chris Lee, Petr Votava, et al. "A Trust-Powered Technique to Facilitate Scientific Tool Discovery and Recommendation." International Journal of Web Services Research 12, no. 3 (2015): 25–47. http://dx.doi.org/10.4018/ijwsr.2015070102.

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While the open science community engenders many similar scientific tools as services, how to differentiate them and help scientists select and reuse existing software services developed by peers remains a challenge. Most of the existing service discovery approaches focus on finding candidate services based on functional and non-functional requirements as well as historical usage analysis. Complementary to the existing methods, this paper proposes to leverage human trust to facilitate software service selection and recommendation. A trust model is presented that leverages the implicit human fac

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Pan, Zhiwen, Jiangtian Li, Yiqiang Chen, Jesus Pacheco, Lianjun Dai, and Jun Zhang. "Knowledge discovery in sociological databases." International Journal of Crowd Science 3, no. 3 (2019): 315–32. http://dx.doi.org/10.1108/ijcs-09-2019-0023.

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Purpose The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets. Design/methodology/a

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NASRAOUI, OLFA, and RAGHU KRISHNAPURAM. "AN EVOLUTIONARY APPROACH TO MINING ROBUST MULTI-RESOLUTION WEB PROFILES AND CONTEXT SENSITIVE URL ASSOCIATIONS." International Journal of Computational Intelligence and Applications 02, no. 03 (2002): 339–48. http://dx.doi.org/10.1142/s1469026802000646.

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We present a technique for simultaneously mining Web navigation patterns and maximally frequent context-sensitive itemsets (URL associations) from the historic user access data stored in Web server logs. A new hierarchical clustering technique that exploits the symbiosis between clusters in feature space and genetic biological niches in nature, called Hierarchical Unsupervised Niche Clustering (H-UNC) is presented. We use H-UNC as part of a complete system of knowledge discovery in Web usage data. Our approach does not necessitate fixing the number of clusters in advance, is insensitive to ini

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Shaik, Abdul Naveed, and Ansar Ali Khan. "Physiologically based pharmacokinetic (PBPK) modeling and simulation in drug discovery and development." ADMET and DMPK 7, no. 1 (2019): 1–3. http://dx.doi.org/10.5599/admet.667.

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Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic or physiology based mathematical modeling technique which integrates the knowledge from both drug-based properties including physiochemical and biopharmaceutical properties and system based or physiological properties to generate a model for predicting the absorption, distribution, metabolism and excretion (ADME) properties of a drug as well as pharmacokinetic behavior of a drug in preclinical species and humans.

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Singh, Dharmpal. "An Effort to Design an Integrated System to Extract Information Under the Domain of Metaheuristics." International Journal of Applied Evolutionary Computation 8, no. 3 (2017): 13–52. http://dx.doi.org/10.4018/ijaec.2017070102.

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The main objective of this work is to develop an integrated system that is capable of extracting precise information (knowledge) based on any stored information using the techniques of data mining and soft computing. For the purpose of extracting precise information based on some stored information, it has been further observed that the research work related to the area of knowledge discovery based on certain information with the help of a particular data mining or soft computing model has been done, but the performance based on the particular soft computing or data mining model has not been r

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Ltifi, Hela, Emna Ben Mohamed, and Mounir ben Ayed. "Interactive visual knowledge discovery from data-based temporal decision support system." Information Visualization 15, no. 1 (2015): 31–50. http://dx.doi.org/10.1177/1473871614567794.

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The article aims to present a generic interactive visual analytics solution that provides temporal decision support using knowledge discovery from data modules together with interactive visual representations. It bases its design decisions on classification of visual representation techniques according to the criteria of temporal data type, periodicity, and dimensionality. The design proposal is applied to an existing medical knowledge discovery from data–based decision support system aiming at assisting physicians in the fight against nosocomial infections in the intensive care units. Our sol

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Atefi, Soodeh, Sakshyam Panda, Emmanouil Panaousis, and Aron Laszka. "Principled Data-Driven Decision Support for Cyber-Forensic Investigations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5010–17. http://dx.doi.org/10.1609/aaai.v37i4.25628.

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In the wake of a cybersecurity incident, it is crucial to promptly discover how the threat actors breached security in order to assess the impact of the incident and to develop and deploy countermeasures that can protect against further attacks. To this end, defenders can launch a cyber-forensic investigation, which discovers the techniques that the threat actors used in the incident. A fundamental challenge in such an investigation is prioritizing the investigation of particular techniques since the investigation of each technique requires time and effort, but forensic analysts cannot know wh

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Lamghari, Zineb. "Process Mining: Auditing Approach Based on Process Discovery Using Frequency Paths Concept." ASM Science Journal 17 (November 2, 2022): 1–11. http://dx.doi.org/10.32802/asmscj.2022.1225.

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In the company environment, the management team is responsible for producing normative models. The normative model is considered a standard model that aims at auditing all business processes in the same context. In this regard, the audit operation encompasses four process mining activities, in a hybrid evaluation (offline and online), which are the detect, the check, the compare, and the promote activities. This is still well performed for structured business processes. Otherwise, complex processes may deviate from the initial defined normative model context. Indeed, the latter must be refined

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Li, Qiao, and Junming Liu. "Development of an Intelligent NLP-Based Audit Plan Knowledge Discovery System." Journal of Emerging Technologies in Accounting 17, no. 1 (2019): 89–97. http://dx.doi.org/10.2308/jeta-52665.

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ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted conten

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Ding, Zhipeng, Hongxia Yun, and Enze Li. "A multimedia knowledge discovery-based optimal scheduling approach considering visual behavior in smart education." Mathematical Biosciences and Engineering 20, no. 3 (2023): 5901–16. http://dx.doi.org/10.3934/mbe.2023254.

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<abstract> <p>Nowadays, the convergence of intelligent computing technique and education has been a hot concern for both academia and industry, producing the conception of smart education. It is predictable that automatic planning and scheduling for course contents are the most practical important task for smart education. As online and offline educational activities are visual behaviors, it remains challenging to capture and extract principal features. To breakthrough current barriers, this paper combines the visual perception technology and data mining theory, and proposes a mult

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Lamghari, Zineb. "An Integrated Approach for Discovering Process Models According to Business Process Types." ASM Science Journal 16 (July 26, 2021): 1–14. http://dx.doi.org/10.32802/asmscj.2021.767.

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Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Managem

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Li, Jing Min, Jin Yao, and Yong Mou Liu. "A Model for Acquisition of Implicit Design Knowledge Based on KDD." Materials Science Forum 505-507 (January 2006): 505–10. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.505.

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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

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Smiti, Abir, and Zied Elouedi. "Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems." Theoretical Computer Science 817 (May 2020): 24–32. http://dx.doi.org/10.1016/j.tcs.2019.06.026.

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Naik, Midde Venkateswarlu, D. Vasumathi, and A. P. Siva Kumar. "An Improved Intelligent Approach to Enhance the Sentiment Classifier for Knowledge Discovery Using Machine Learning." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (2020): 582–93. http://dx.doi.org/10.2174/2210327910999200528114552.

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Aims: The proposed research work is on an evolutionary enhanced method for sentiment or emotion classification on unstructured review text in the big data field. The sentiment analysis plays a vital role for current generation of people for extracting valid decision points about any aspect such as movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification through Support Vector Machine (SVM). The current approach performance is evaluated with statistical me

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Omidipoor, Morteza, Ara Toomanian, Najmeh Neysani Samany, and Ali Mansourian. "Knowledge Discovery Web Service for Spatial Data Infrastructures." ISPRS International Journal of Geo-Information 10, no. 1 (2020): 12. http://dx.doi.org/10.3390/ijgi10010012.

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The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems.

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Ullah, Zafar, Muhammad Uzair, and Arshad Mehmood. "Extraction of Key Motifs as a Preview from 2017 Nobel Prize Winning Novel, ‘Never Let Me Go’." Journal of Research in Social Sciences 7, no. 2 (2021): 83–98. http://dx.doi.org/10.52015/jrss.7i2.80.

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Word clouds manifest interactive visuals along with their statistical data. Thus knowledge discovery and aesthetic data visualization interlink to produce interactive word cloud which is an interesting, textual, statistical and visual data. This study aims to generate interactive word cloud—Cirrus—on the basis of statistical data to preview text of the novel for readers. So cirrus tool is selected from Voyant open access tools to produce interactive statistical word cloud. Then the generated word cloud and statistical data are analyzed with mixed method and its analysis draws insight from Rake

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Sanchez-Segura, Maria-Isabel, Roxana González-Cruz, Fuensanta Medina-Dominguez, and German-Lenin Dugarte-Peña. "Valuable Business Knowledge Asset Discovery by Processing Unstructured Data." Sustainability 14, no. 20 (2022): 12971. http://dx.doi.org/10.3390/su142012971.

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Modern organizations are challenged to enact a digital transformation and improve their competitiveness while contributing to the ninth Sustainable Development Goal (SGD), “Build resilient infrastructure, promote sustainable industrialization and foster innovation”. The discovery of hidden process data’s knowledge assets may help to digitalize processes. Working on a valuable knowledge asset discovery process, we found a major challenge in that organizational data and knowledge are likely to be unstructured and undigitized, constraining the power of today’s process mining methodologies (PMM).

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Guo, Yu Dong. "Prototype System of Knowledge Management Based on Data Mining." Applied Mechanics and Materials 411-414 (September 2013): 251–54. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.251.

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Knowledge is a very crucial resource to promote economic development and society progress which includes facts, information, descriptions, or skills acquired through experience or education. With knowledge has being increasingly prominent, knowledge management has become important measure for the core competences promotion of a corporation. The paper begins with knowledge managements definition, and studies the process of knowledge discovery from databases (KDD),data mining techniques and SECI(Socialization, Externalization, Combination, Internalization) model of knowledge dimensions. Finally,

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Liu, Han, Alexander Gegov, and Mihaela Cocea. "Rule Based Networks: An Efficient and Interpretable Representation of Computational Models." Journal of Artificial Intelligence and Soft Computing Research 7, no. 2 (2017): 111–23. http://dx.doi.org/10.1515/jaiscr-2017-0008.

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Abstract Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modell

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V, Bindhu. "Artificial Intelligence based Business Process Automation for Enhanced Knowledge Management." June 2021 3, no. 2 (2021): 65–78. http://dx.doi.org/10.36548/jeea.2021.2.001.

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A customer relationship management (CRM) system based on Artificial Intelligence (AI) is used to discover critical success factors (CSF) in order to improve the automated business process and deliver better knowledge management (KM). Moreover, different factors contribute towards achieving efficient knowledge management in CRM systems with AI schemes. Identifying the key elements may be accomplished in a variety of ways. For this purpose, Delphi technique, nominal group technique, and brainstorming approach are used. Using the interpretive structural modelling (ISM) approach, ten key variables

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Chun, Se-Hak, and Young-Woong Ko. "Geometric Case Based Reasoning for Stock Market Prediction." Sustainability 12, no. 17 (2020): 7124. http://dx.doi.org/10.3390/su12177124.

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Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting n

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Rajakumari, D. "PERFORMANCE VALIDATION OF PRIOR QUANTIZATION TECHNIQUES IN OUTLIERS CLASSIFICATION USING WDBC DATASET." International Journal of Engineering Technologies and Management Research 5, no. 4 (2020): 48–56. http://dx.doi.org/10.29121/ijetmr.v5.i4.2018.207.

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Data mining is the process of analyzing enormous data and summarizing it into the useful knowledge discovery and the task of data mining approaches is growing quickly, particularly classification techniques very efficient, way to classifying the data, which is important in the decision-making process for medical practitioners. This study presents the quantization and validation (OQV) techniques for fast outlier detection in large size WDBC data sets. The distance metrics utilization makes the algorithm as the linear one for various objects and assures the sequential scanning. The inclusion of

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Kraker, Peter, Christopher Kittel, and Asura Enkhbayar. "Open Knowledge Maps: Creating a Visual Interface to the World’s Scientific Knowledge Based on Natural Language Processing." 027.7 Zeitschrift für Bibliothekskultur 4, no. 2 (2016): 98–103. http://dx.doi.org/10.12685/027.7-4-2-157.

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The goal of Open Knowledge Maps is to create a visual interface to the world’s scientific knowledge. The base for this visual interface consists of so-called knowledge maps, which enable the exploration of existing knowledge and the discovery of new knowledge. Our open source knowledge mapping software applies a mixture of summarization techniques and similarity measures on article metadata, which are iteratively chained together. After processing, the representation is saved in a database for use in a web visualization. In the future, we want to create a space for collective knowledge mapping

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Hu, Ping, Dong-xiao Gu, and Yu Zhu. "Collaborative Case-Based Reasoning for Knowledge Discovery of Elders Health Assessment System." Open Biomedical Engineering Journal 8, no. 1 (2014): 68–74. http://dx.doi.org/10.2174/1874120701408010068.

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The existing Elders Health Assessment (EHA) system based on single-case-library reasoning has low intelligence level, poor coordination, and limited capabilities of assessment decision support. To effectively support knowledge reuse of EHA system, this paper proposes collaborative case reasoning and applies it to the whole knowledge reuse process of EHA system. It proposes a multi-case library reasoning application framework of EHA knowledge reuse system, and studies key techniques such as case representation, case retrieval algorithm, case optimization and correction, and reuse etc.. In the a

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Lage, Olga, María Ramos, Rita Calisto, Eduarda Almeida, Vitor Vasconcelos, and Francisca Vicente. "Current Screening Methodologies in Drug Discovery for Selected Human Diseases." Marine Drugs 16, no. 8 (2018): 279. http://dx.doi.org/10.3390/md16080279.

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The increase of many deadly diseases like infections by multidrug-resistant bacteria implies re-inventing the wheel on drug discovery. A better comprehension of the metabolisms and regulation of diseases, the increase in knowledge based on the study of disease-born microorganisms’ genomes, the development of more representative disease models and improvement of techniques, technologies, and computation applied to biology are advances that will foster drug discovery in upcoming years. In this paper, several aspects of current methodologies for drug discovery of antibacterial and antifungals, an

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Karthikeyani Visalakshi N., Shanthi S., and Lakshmi K. "MapReduce-Based Crow Search-Adopted Partitional Clustering Algorithms for Handling Large-Scale Data." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–23. http://dx.doi.org/10.4018/ijcini.20211001.oa32.

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Cluster analysis is the prominent data mining technique in knowledge discovery and it discovers the hidden patterns from the data. The K-Means, K-Modes and K-Prototypes are partition based clustering algorithms and these algorithms select the initial centroids randomly. Because of its random selection of initial centroids, these algorithms provide the local optima in solutions. To solve these issues, the strategy of Crow Search algorithm is employed with these algorithms to obtain the global optimum solution. With the advances in information technology, the size of data increased in a drastic

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Ltifi, Hela, Mounir Ben Ayed, Ghada Trabelsi, and Adel M. Alimi. "Perspective Wall Technique for Visualizing and Interpreting Medical Data." International Journal of Knowledge Discovery in Bioinformatics 3, no. 2 (2012): 45–61. http://dx.doi.org/10.4018/jkdb.2012040104.

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Increasing the improvement of confidence and comprehensibility of medical data as well as the possibility of using the human capacities in medical pattern recognition is a significant interest for the coming years. In this context, we have created a visual knowledge discovery from databases application. It has been developed to efficiently and accurately understand a large collection of fixed and temporal patients’ data in the Intensive Care Unit in order to prevent the nosocomial infection occurrence. It is based on data visualization technique which is the perspective wall. Its application i

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Hussain, Zahraa Faiz, Hind Raad Ibraheem, Mohammad Alsajri, et al. "A new model for iris data set classification based on linear support vector machine parameter's optimization." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 1079. http://dx.doi.org/10.11591/ijece.v10i1.pp1079-1084.

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Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary proble

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D'Abrusco, Raffaele, Giuseppina Fabbiano, Omar Laurino, and Francesco Massaro. "Knowledge Discovery Workflows in the Exploration of Complex Astronomical Datasets." Proceedings of the International Astronomical Union 10, H16 (2012): 681–82. http://dx.doi.org/10.1017/s1743921314012885.

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AbstractThe massive amount of data produced by the recent multi-wavelength large-area surveys has spurred the growth of unprecedentedly massive and complex astronomical datasets that are proving the traditional data analysis techniques more and more inadequate. Knowledge discovery techniques, while relatively new to astronomy, have been successfully applied in several other quantitative disciplines for the determination of patterns in extremely complex datasets. The concerted use of different unsupervised and supervised machine learning techniques, in particular, can be a powerful approach to

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