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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2019年第2期1卷 29-38页

作者： Zhang, Zaifang Chai, Nana Liu, Yuan Xia, Beixin Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Shanghai Univ Sch Management 99 Shangda Rd Shanghai 200444 Peoples R China

Manufacturers tend to bundle a product with its related services as a product service system (PSS), to create more values for customers and gain competitive advantages for themselves. Configuration design is the key process of PSS development. Configuring a PSS involves selecting and combining appropriate product and service components, to satisfy individual customer requirements. This study studies the mapping relationship between customer requirement attributes and PSS base types in PSS for CNC machine tools, which provides a great reference value for engineers in configuration design. Owing to the high complexity and non-linearity between customer requirement and product, an integrated intelligent learning method based on a priori algorithm and knowledge-based artificial neural network (ANN) is proposed in this study. First, the data of historical configuration instance data sets are processed and then a priori algorithm is used to extract the effective rules as domain knowledge. Domain knowledge is used to build the initial structure of ANN. Moreover, data sets are used to further optimize the network. The knowledge-based ANN is used to realize the mapping between customer requirement attributes and PSS base types. The proposed method is validated in the selection of the PSS base type for CNC machine tools.

关键词： customer services neural nets computerised numerical control machine tools production engineering computing learning (artificial intelligence) product customisation product service system configuration design PSS development selecting combining appropriate product individual customer requirements customer requirement attributes PSS base type CNC machine tools a priori algorithm knowledge-based artificial neural network historical configuration instance data sets domain knowledge knowledge-based ANN base types selection related services

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Itemsets-Based Amharic Document Categorization Using an Extended A priori algorithm 6th

Itemsets-Based Amharic Document Categorization Using an Exte...

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6th Language and Technology Conference (LTC)

作者： Hailu, Abraham Assabie, Yaregal Univ Addis Ababa Dept Comp Sci Addis Ababa Ethiopia

ISBN: (纸本)9783319438085;9783319438078

Document categorization is gaining importance due to the large volume of electronic information which requires automatic organization and pattern identification. Due to the morphological complexity of the language, automatic categorization of Amharic documents has become a difficult talk to carry out. This paper presents a system that categorizes Amharic documents based on the frequency of itemsets obtained after analyzing the morphology of the language. We selected seven categories into which a given document is to be classified. The task of categorization is achieved by employing an extended version of a priori algorithm which had been traditionally used for the purpose of knowledge mining in the form of association rules. The system is tested with a corpus containing Amharic news documents and experimental results are reported.

关键词： Amharic language processing Text categorization Document classification a priori algorithm Itemsets

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An enhanced a priori algorithm for mining multidimensional association rules

An enhanced a Priori algorithm for mining multidimensional a...

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25th International Conference on Information Technology Interfaces

作者： Janas, JM Univ Bundeswehr Munchen Fak Wirtschafts & Org Wissensch D-85577 Neubiberg Germany

ISBN: (纸本)9539676967

In this paper, two concepts front different research areas are brought together, namely functional dependencies which are a class of integrity constraints that have gained fundamental importance for relational database design and association rules which are a class of patterns which has been studied rigorously in data mining. It will be shown that functional dependencies may be used to logically infer new association rules from given ones. This observation will then be employed to propose a new variant of the best known algorithm for association rule mining, the so-called a priori algorithm.

关键词： data mining association rules a priori algorithm relational databases functional dependencies

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Automatic Detection Method of Website Vulnerabilities Based on an Associated Data Drive

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Journal of Web Engineering 2025年第2期24卷 217-242页

作者： Li, Xiaoli Zhao, Ling Shen, Haobin Du, Hanlin Guo, Zhida Huizhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Huizhou 516000 China

In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition performance. It not only significantly reduces the clustering analysis time, but also ensures the accuracy and timeliness of vulnerability detection. © 2025 River Publishers.

关键词： a priori algorithm automated detection clustering algorithm convolutional neural network website website vulnerability

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PBP: A Type of BP Neural Network algorithm Based on priori Triggering

PBP: A Type of BP Neural Network Algorithm Based on Priori T...

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International Conference on Advanced Materials and Information Technology Processing (AMITP 2011)

作者： Chen Xiaomei Gang, Dang Yang, Tian Natl Univ Def Technol Sch Comp Sci Changsha 410073 Peoples R China

ISBN: (纸本)9783037851579

The algorithm of anomaly detection for large scale networks is a key way to promptly detect the abnormal traffic flows. In this paper, priori triggered BP neural network algorithm(PBP) is analyzed for the purpose of dealing with the problems caused by typical algorithms that are not able to adapt and learn;detect with high precision;provide high level of correctness. PBP uses K-Means and PCA to trigger self-adapting and learning ability, and also, it uses historical neuron parameter to initialize the neural network, so that it use the trained network to detect the abnormal traffic flows. According to experiments, PBP can obtain a higher level of correctness of detection than priori algorithm, and it can adapt itself according to different network environments.

关键词： a priori algorithm Self-learning network traffic anomaly detection

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PBP:A Type of BP Neural Network algorithm Based on priori Triggering

PBP:A Type of BP Neural Network Algorithm Based on Priori Tr...

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The 2011 International Conference on Advanced Materials and Information Technology Processing(AMITP 2011)

作者： Chen Xiaomei Dang Gang Tian Yang School of Computer Science National University of Defense Technology

ISBN: (纸本)9783037851579

关键词： a priori algorithm Self-learning network traffic anomaly detection

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Multi-way association extraction and visualization from biological text documents using hyper-graphs: Applications to genetic association studies for diseases

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2010年第3期49卷 145-154页

作者： Mukhopadhyay, Snehasis Palakal, Mathew Maddu, Kalyan Indiana Univ Purdue Univ Indianapolis Dept Comp & Informat Sci Indianapolis IN 46202 USA

Objectives: Biological research literature, as in many other domains of human endeavor, represents a rich, ever growing source of knowledge. An important form of such biological knowledge constitutes associations among biological entities such as genes, proteins, diseases, drugs and chemicals, etc. There has been a considerable amount of recent research in extraction of various kinds of binary associations (e.g., gene-gene, gene-protein, protein-protein, etc.) using different text mining approaches. However, an important aspect of such associations (e.g., "gene A activates protein B") is identifying the context in which such associations occur (e.g., "gene A activates protein B in the context of disease C in organ D under the influence of chemical E"). Such contexts can be represented appropriately by a multi-way relationship involving more than two objects (e.g., objects A, B, C, D, E) rather than usual binary relationship (objects A and B). Methods: Such multi-way relations naturally lead to a hyper-graph representation of the knowledge rather than a binary graph. The hyper-graph based multi-way knowledge extraction from biological text literature represents a computationally difficult problem (due to its combinatorial nature) which has not received much attention from the Bioinformatics research community. In this paper, we describe and compare two different approaches to such multi-way hyper-graph extraction: one based on an exhaustive enumeration of all multi-way hyper-edges and the other based on an extension of the well-known a priori algorithm for structured data to the case unstructured textual data. We also present a representative graph based approach towards visualizing these genetic association hyper-graphs. Results: Two case studies are conducted for two biomedical problems (related to the diseases of lung cancer and colorectal cancer respectively), illustrating that the latter approach (using the text-based A priori method) identifies the same hyper-e

关键词： Hyper-graphs Representative graphs a priori algorithm Vector space model Genetic associations Lung cancer Colorectal cancer

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Association rule mining using binary particle swarm optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第8期26卷 1832-1840页

作者： Sarath, K. N. V. D. Ravi, Vadlamani Inst Dev & Res Banking Technol Hyderabad 500057 Andhra Pradesh India

In this paper, we developed a binary particle swarm optimization (BPSO) based association rule miner. Our BPSO based association rule miner generates the association rules from the transactional database by formulating a combinatorial global optimization problem, without specifying the minimum support and minimum confidence unlike the a priori algorithm. Our algorithm generates the best M rules from the given database, where M is a given number. The quality of the rule is measured by a fitness function defined as the product of support and confidence. The effectiveness of our algorithm is tested on a real life bank dataset from commercial bank in India and three transactional datasets viz, books database, food items dataset and dataset of the general store taken from literature. Based on the results, we infer that our algorithm can be used as an alternative to the a priori algorithm and the FP-growth algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization Association rule mining Support Confidence a priori algorithm FP-growth algorithm

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Interpreting the web-mining results by cognitive map and association rule approach

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INFORMATION PROCESSING & MANAGEMENT 2011年第4期47卷 482-490页

作者： Lee, Kun Chang Lee, Sangjae Sejong Univ Sch Business Adm Seoul 143747 South Korea Sungkyunkwan Univ Dept Interact Sci SKK Business Sch Seoul 110745 South Korea

A variety of the web-mining techniques are now being extensively utilized to extract useful knowledge about customer behaviors on the Internet. However, the naive interpretation of the web-mining results would lead to poor decision on customer behaviors, which is likely to cause waste of time and efforts on managing electronic commerce strategy. To overcome this pitfall, this study proposes using the cognitive map-based interpretation of the web-mining results. Conventional approach to obtaining the web-mining results is based on the association rule approach (ARA), while the cognitive map approach (CMA) is believed to provide more robust support in interpreting the web-mining results. Therefore, to compare the interpretation capability of the two approaches, the four constructs such as perceived usefulness, causality, information richness, users' attitude and intention to use the approaches are adopted in the research model and tested against the questionnaire data. The test results obtained through applying the structural equation models reveal that CMA is comparable to ARA and the cognitive map has a great potential in helping enrich the interpretation of the web mining results and build more effective Internet business strategy. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Web-mining Cognitive map approach (CMA) Association rule approach (ARA) a priori algorithm Structural equation modeling

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Using modern IT tools to assess the awareness of MDs on radiation issues and plan a continuous education programme

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Health Informatics Journal 2001年第4期7卷 146-151页

作者： Bamidis, P.D. Psarouli, E. Stilou, S. Department of Computer Science CITY Liberal Studies Affiliated Institution of the University of Sheffield United Kingdom Department of Nuclear Medicine Ippocration General Hospital Thessaloniki Greece Laboratory of Medical Informatics Medical School University of Thessaloniki Thessaloniki Greece

Radiation is significantly involved in a number of common medical tests and procedures. Knowledge of issues concerned with ionizing radiation is, therefore, mandatory in clinical practice. Is there a way to access whether university-level educational programmes concerning medical physics suffice in covering the practical requirements or not? One of the key issues involved in the assessment of the above procedure, however, is how to make use of available technology in order to conduct the assessment as efficiently and as effectively as possible. In previous work, we devised a sample manual questionnaire aimed at retrieving information regarding the status of radiation awareness, but also investigated the feasibility of applying a robust methodology in the analysis of the results. In this paper, we extend the above mentioned methodology to other areas of MD activity and utilize the question ‘how familiar are MDs with radiation?’ as an example, in order to demonstrate the importance of health information management in the modern era of e-health. Three stages or aspects of information management are considered: (i) efficient online information collection, (ii) effective and robust analysis based on Web databases, and finally (iii) extraction of rules and knowledge using data mining techniques and home-made software that facilitates the retrieval of associations. The latter stage is then further exploited in order to design an improved and realistic plan for the process of continuous education of MDs and health professionals in general. © 2001, Sage Publications. All rights reserved.

关键词： a priori algorithm association rules data mining education management Health information management Internet survey radiation awareness

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