Relevant bibliographies by topics / Agent-based data mining

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Academic literature on the topic 'Agent-based data mining'

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Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Agent-based data mining"

M., Inbavalli. "An Intelligent Agent based Mining Techniques for Distributed Data Mining." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 610–17. http://dx.doi.org/10.5373/jardcs/v12sp4/20201527.

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Sridhar, S. "Improving diagnostic accuracy using agent-based distributed data mining system." Informatics for Health and Social Care 38, no. 3 (September 7, 2012): 182–95. http://dx.doi.org/10.3109/17538157.2012.716110.

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Devasekhar, V., and P. Natarajan. "Multi-agent based data mining aggregation approaches using machine learning techniques." International Journal of Engineering & Technology 7, no. 3 (June 23, 2018): 1136. http://dx.doi.org/10.14419/ijet.v7i3.9631.

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Data Mining is an extraction of important knowledge from the various databases using different kinds of approaches. In the multi agent, distributed mining the knowledge aggregation is one of challenging task. This paper tries to optimize the problem of aggregation and boils down into the solution, which is derived based on the machine learning statistical features of each agents. However, in this paper a novel optimization algorithm called Multi-Agent Based Data Mining Aggregation (MABDA) is used for present day’s scenarios. The MBADA algorithm has agents which collect extracted knowledge and summarizes the various levels of agent’s cluster data into an aggregation with maximum accuracies. To prove the effectiveness of the proposed algorithm, the experimental results are compared with relatively existing methods.

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Sherief, Abdallah. "Mining Dynamics: Using Data Mining Techniques to Analyze Multi-agent Learning." Journal of Intelligent Systems 26, no. 4 (September 26, 2017): 613–24. http://dx.doi.org/10.1515/jisys-2016-0136.

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AbstractAnalyzing the learning dynamics in multi-agent systems (MASs) has received growing attention in recent years. Theoretical analysis of the dynamics was only possible in simple domains and simple algorithms. When one or more of these restrictions do not apply, theoretical analysis becomes prohibitively difficult, and researchers rely on experimental analysis instead. In experimental analysis, researchers have used some global performance metric(s) as a rough approximation to the internal dynamics of the adaptive MAS. For example, if the overall payoff improved over time and eventually appeared to stabilize, then the learning dynamics were assumed to be stable as well. In this paper, we promote a middle ground between the thorough theoretical analysis and the high-level experimental analysis. We introduce the concept of mining dynamics and propose data-mining-based methodologies to analyze multi-agent learning dynamics. Using our methodologies, researchers can identify clusters of learning parameter values that lead to similar performance, and discover frequent sequences in agent dynamics. We verify the potential of our approach using the well-known iterated prisoner’s dilemma (with multiple states) domain.

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Lin, Hsueh-Yuan, and Sheng-Yuan Yang. "A cloud-based energy data mining information agent system based on big data analysis technology." Microelectronics Reliability 97 (June 2019): 66–78. http://dx.doi.org/10.1016/j.microrel.2019.03.010.

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Lin, Hsueh-Yuan, and Sheng-Yuan Yang. "A Smart Cloud-Based Energy Data Mining Agent Using Big Data Analysis Technology." Smart Science 7, no. 3 (April 14, 2019): 175–83. http://dx.doi.org/10.1080/23080477.2019.1600112.

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Fuqiang, Yue. "The Research on Distributed Data Stream Mining based on Mobile Agent." Procedia Engineering 23 (2011): 103–8. http://dx.doi.org/10.1016/j.proeng.2011.11.2473.

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Sun, Xiao Yan, Long Li, and Ping Ping Liu. "Research on Fault Diagnosis for Power Transmission Based on Mass Data Mining." Applied Mechanics and Materials 271-272 (December 2012): 1623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1623.

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A Multi-Agent based transmission fault diagnosis system is researched in this paper. Many data digging analysis methods are employed, combined with data warehouse, OLAP and Multi-Agent technology. An intelligent decision supporting system for monitoring transmission network data is built. Data digging method is used to intelligently analyze and process fault data in the data warehouse, and Agent technology is used to realize data collection, pretreatment, inquiry, knowledge Automatic extraction, mining and other functions, which makes the whole mining process intellectual and intelligent. It aids transmission management with decision-making, thus to make the monitoring and repair of power grid fault more timely and accurate.

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Mazumdar, Bireshwar Dass, and R. B. Mishra. "Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods." International Journal of Intelligent Information Technologies 6, no. 4 (October 2010): 46–70. http://dx.doi.org/10.4018/jiit.2010100104.

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The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.

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Ellouzi, Hamdi, Hela Ltifi, and Mounir Ben Ayed. "Multi-agent modelling of decision support systems based on visual data mining." Multiagent and Grid Systems 13, no. 1 (April 12, 2017): 31–45. http://dx.doi.org/10.3233/mgs-170260.

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Dissertations / Theses on the topic "Agent-based data mining"

Chaimontree, Santhana. "Multi-agent data mining with negotiation : a study in multi-agent based clustering." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/7673/.

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Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collaborate to produce a ``best'' set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a ``best'' cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents ``swap'' unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach.

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Alfuhaid, Abdulaziz Ataallah. "AN AGENT-BASED SYSTEMATIC ENSEMBLE APPROACH FOR AUTO AUCTION PREDICTION." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1542560217326084.

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Hassouna, Mohammed Bassam. "Agent based modelling and simulation : an examination of customer retention in the UK mobile market." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6344.

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Customer retention is an important issue for any business, especially in mature markets such as the UK mobile market where new customers can only be acquired from competitors. Different methods and techniques have been used to investigate customer retention including statistical methods and data mining. However, due to the increasing complexity of the mobile market, the effectiveness of these techniques is questionable. This study proposes Agent-Based Modelling and Simulation (ABMS) as a novel approach to investigate customer retention. ABMS is an emerging means of simulating behaviour and examining behavioural consequences. In outline, agents represent customers and agent relationships represent processes of agent interaction. This study follows the design science paradigm to build and evaluate a generic, reusable, agent-based (CubSim) model to examine the factors affecting customer retention based on data extracted from a UK mobile operator. Based on these data, two data mining models are built to gain a better understanding of the problem domain and to identify the main limitations of data mining. This is followed by two interrelated development cycles: (1) Build the CubSim model, starting with modelling customer interaction with the market, including interaction with the service provider and other competing operators in the market; and (2) Extend the CubSim model by incorporating interaction among customers. The key contribution of this study lies in using ABMS to identify and model the key factors that affect customer retention simultaneously and jointly. In this manner, the CubSim model is better suited to account for the dynamics of customer churn behaviour in the UK mobile market than all other existing models. Another important contribution of this study is that it provides an empirical, actionable insight on customer retention. In particular, and most interestingly, the experimental results show that applying a mixed customer retention strategy targeting both high value customers and customers with a large personal network outperforms the traditional customer retention strategies, which focuses only on the customer‘s value.

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Nicholson, Scott. "Creating a Criterion-Based Information Agent Through Data Mining for Automated Identification of Scholarly Research on the World Wide Web." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2459/.

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This dissertation creates an information agent that correctly identifies Web pages containing scholarly research approximately 96% of the time. It does this by analyzing the Web page with a set of criteria, and then uses a classification tree to arrive at a decision. The criteria were gathered from the literature on selecting print and electronic materials for academic libraries. A Delphi study was done with an international panel of librarians to expand and refine the criteria until a list of 41 operationalizable criteria was agreed upon. A Perl program was then designed to analyze a Web page and determine a numerical value for each criterion. A large collection of Web pages was gathered comprising 5,000 pages that contain the full work of scholarly research and 5,000 random pages, representative of user searches, which do not contain scholarly research. Datasets were built by running the Perl program on these Web pages. The datasets were split into model building and testing sets. Data mining was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. The models were created with the model datasets and then tested against the test dataset. Precision and recall were used to judge the effectiveness of each model. In addition, a set of pages that were difficult to classify because of their similarity to scholarly research was gathered and classified with the models. The classification tree created the most effective classification model, with a precision ratio of 96% and a recall ratio of 95.6%. However, logistic regression created a model that was able to correctly classify more of the problematic pages. This agent can be used to create a database of scholarly research published on the Web. In addition, the technique can be used to create a database of any type of structured electronic information.

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Mortensen, Clifton H. "A Computational Fluid Dynamics Feature Extraction Method Using Subjective Logic." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2208.

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Computational fluid dynamics simulations are advancing to correctly simulate highly complex fluid flow problems that can require weeks of computation on expensive high performance clusters. These simulations can generate terabytes of data and pose a severe challenge to a researcher analyzing the data. Presented in this document is a general method to extract computational fluid dynamics flow features concurrent with a simulation and as a post-processing step to drastically reduce researcher post-processing time. This general method uses software agents governed by subjective logic to make decisions about extracted features in converging and converged data sets. The software agents are designed to work inside the Concurrent Agent-enabled Feature Extraction concept and operate efficiently on massively parallel high performance computing clusters. Also presented is a specific application of the general feature extraction method to vortex core lines. Each agent's belief tuple is quantified using a pre-defined set of information. The information and functions necessary to set each component in each agent's belief tuple is given along with an explanation of the methods for setting the components. A simulation of a blunt fin is run showing convergence of the horseshoe vortex core to its final spatial location at 60% of the converged solution. Agents correctly select between two vortex core extraction algorithms and correctly identify the expected probabilities of vortex cores as the solution converges. A simulation of a delta wing is run showing coherently extracted primary vortex cores as early as 16% of the converged solution. Agents select primary vortex cores extracted by the Sujudi-Haimes algorithm as the most probable primary cores. These simulations show concurrent feature extraction is possible and that intelligent agents following the general feature extraction method are able to make appropriate decisions about converging and converged features based on pre-defined information.

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Jiang, Lingxia. "Research on distributed data mining system and algorithm based on multi-agent." Thèse, 2009. http://constellation.uqac.ca/137/1/030120838.pdf.

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Data mining means extracting hidden, previous unknown knowledge and rules with potential value to decision from mass data in database. Association rule mining is a main researching area of data mining area, which is widely used in practice. With the development of network technology and the improvement of level of IT application, distributed database is commonly used. Distributed data mining is mining overall knowledge which is useful for management and decision from database distributed in geography. It has become an important issue in data mining analysis. Distributed data mining can achieve a mining task with computers in different site on the internet. It can not only improve the mining efficiency, reduce the transmitting amount of network data, but is also good for security and privacy of data. Based on related theories and current research situation of data mining and distributed data mining, this thesis will focus on analysis on the structure of distributed mining system and distributed association rule mining algorithm. This thesis first raises a structure of distributed data mining system which is base on multi-agent. It adopts star network topology, and realize distributed saving mass data mining with multi-agent. Based on raised distributed data mining system, this these brings about a new distributed association rule mining algorithm?RK-tree algorithm. RK-tree algorithm is based on the basic theory of twice knowledge combination. Each sub-site point first mines local frequency itemset from local database, then send the mined local frequency itemset to the main site point. The main site point combines those local frequency itemset and get overall candidate frequency itemset, and send the obtained overall candidate frequency itemset to each sub-site point. Each sub-site point count the supporting rate of those overall candidate frequency itemset and sent it back to the main site point. At last, the main site point combines the results sent by sub-site point and gets the overall frequency itemset and overall association rule. This algorithm just needs three times communication between the main and sub-site points, which greatly reduces the amount and times of communication, and improves the efficiency of selection. What's more, each sub-site point can fully use existing good centralized association rule mining algorithm to realize local association rule mining, which can enable them to obtain better local data mining efficiency, as well as reduce the workload. This algorithm is simple and easy to realize. The last part of this thesis is the conclusion of the analysis, as well as the direction of further research.

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Teófilo, Luís Filipe Guimarães. "Building a poker playing agent based on game logs using supervised learning." Dissertação, 2010. http://hdl.handle.net/10216/59566.

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Teófilo, Luís Filipe Guimarães. "Building a poker playing agent based on game logs using supervised learning." Master's thesis, 2010. http://hdl.handle.net/10216/59566.

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"Modeling Frameworks for Supply Chain Analytics." Doctoral diss., 2012. http://hdl.handle.net/2286/R.I.14578.

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abstract: Supply chains are increasingly complex as companies branch out into newer products and markets. In many cases, multiple products with moderate differences in performance and price compete for the same unit of demand. Simultaneous occurrences of multiple scenarios (competitive, disruptive, regulatory, economic, etc.), coupled with business decisions (pricing, product introduction, etc.) can drastically change demand structures within a short period of time. Furthermore, product obsolescence and cannibalization are real concerns due to short product life cycles. Analytical tools that can handle this complexity are important to quantify the impact of business scenarios/decisions on supply chain performance. Traditional analysis methods struggle in this environment of large, complex datasets with hundreds of features becoming the norm in supply chains. We present an empirical analysis framework termed Scenario Trees that provides a novel representation for impulse and delayed scenario events and a direction for modeling multivariate constrained responses. Amongst potential learners, supervised learners and feature extraction strategies based on tree-based ensembles are employed to extract the most impactful scenarios and predict their outcome on metrics at different product hierarchies. These models are able to provide accurate predictions in modeling environments characterized by incomplete datasets due to product substitution, missing values, outliers, redundant features, mixed variables and nonlinear interaction effects. Graphical model summaries are generated to aid model understanding. Models in complex environments benefit from feature selection methods that extract non-redundant feature subsets from the data. Additional model simplification can be achieved by extracting specific levels/values that contribute to variable importance. We propose and evaluate new analytical methods to address this problem of feature value selection and study their comparative performance using simulated datasets. We show that supply chain surveillance can be structured as a feature value selection problem. For situations such as new product introduction, a bottom-up approach to scenario analysis is designed using an agent-based simulation and data mining framework. This simulation engine envelopes utility theory, discrete choice models and diffusion theory and acts as a test bed for enacting different business scenarios. We demonstrate the use of machine learning algorithms to analyze scenarios and generate graphical summaries to aid decision making.
Dissertation/Thesis
Ph.D. Industrial Engineering 2012

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Κόρδαρης, Ιωάννης. "Η αντιμετώπιση της πληροφοριακής υπερφόρτωσης ενός οργανισμού με χρήση ευφυών πρακτόρων." Thesis, 2014. http://hdl.handle.net/10889/7965.

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Η πληροφοριακή υπερφόρτωση των χρηστών αποτελεί βασικό πρόβλημα ενός οργανισμού. Η συσσώρευση μεγάλου όγκου πληροφορίας στα πληροφοριακά συστήματα, προκαλεί στους χρήστες άγχος και υπερένταση, με αποτέλεσμα να δυσχεραίνει την ικανότητά τους για λήψη αποφάσεων. Λόγω αυτού, η επίδραση της πληροφοριακής υπερφόρτωσης στους οργανισμούς είναι καταστροφική και απαιτείται η αντιμετώπισή της. Υπάρχουν διάφοροι τρόποι αντιμετώπισης της πληροφοριακής υπερφόρτωσης όπως τα συστήματα υποστήριξης λήψης αποφάσεων, τα συστήματα φιλτραρίσματος πληροφορίας, οι αποθήκες δεδομένων και άλλες τεχνικές της εξόρυξης δεδομένων και της τεχνητής νοημοσύνης, όπως είναι οι ευφυείς πράκτορες. Οι ευφυείς πράκτορες αποτελούν εφαρμογές που εφάπτονται της τεχνικής νοημοσύνης, οι οποίες έχουν την ικανότητα να δρουν αυτόνομα, συλλέγοντας πληροφορίες, εκπαιδεύοντας τον εαυτό τους και επικοινωνώντας με τον χρήστη και μεταξύ τους. Συχνά, υλοποιούνται πολυπρακτορικά συστήματα προκει-μένου να επιλυθεί ένα πρόβλημα του οργανισμού. Στόχος τους είναι να διευκολύνουν τη λήψη αποφάσεων των χρηστών, προτείνοντας πληροφορίες βάσει των προτιμήσεών τους. Ο σκοπός της παρούσας διπλωματικής εργασίας είναι να αναλύσει σε βάθος τους ευφυείς πράκτορες, σαν μία αποτελεσματική μέθοδο αντιμετώπισης της πληροφοριακής υπερφόρτωσης, να προτείνει πειραματικούς πράκτορες προτά-σεων και να εξετάσει επιτυχημένες υλοποιήσεις. Συγκεκριμένα, παρουσιάζεται ένα ευφυές σύστημα διδασκαλίας για την ενίσχυση του e-Learning/e-Teaching, προτείνεται ένα σύστημα πρακτόρων για τον οργανισμό Flickr, ενώ εξετάζεται το σύστημα προτάσεων του Last.fm και ο αλγόριθμος προτάσεων του Amazon. Τέλος, αναλύεται μια πειραματική έρευνα ενός ευφυούς πράκτορα προτάσεων, ο οποίος αντιμετώπισε με επιτυχία την αντιληπτή πληροφοριακή υπερφόρτωση των χρηστών ενός θεωρητικού ηλεκτρονικού καταστήματος. Τα αποτελέσματα του πειράματος παρουσίασαν την επίδραση της αντιληπτής πληροφοριακής υπερφόρτωσης και του φορτίου πληροφορίας στην ποιότητα επιλογής, στην εμπιστοσύνη επιλογής και στην αντιληπτή αλληλεπίδραση μεταξύ ηλεκτρονικού καταστήματος και χρήστη, ενώ παρατηρήθηκε η καθοριστική συμβολή της χρήσης των ευφυών πρακτόρων στην αντιμετώπιση της πληροφοριακής υπερφόρτωσης.
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Books on the topic "Agent-based data mining"

Chengqi, Zhang, ed. Agent-based hybrid intelligent systems: An agent-based fromework for complex problem solving. New York: Springer, 2004.

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Zili, Zhang. Agent-based hybrid intelligent systems: An agent-based framework for complex problem solving. Berlin: Springer, 2004.

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Duarte, Bouça, and Gafagnão Amaro, eds. Agent-based computing. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Puranam, Phanish. Methodologies for Microstructures. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199672363.003.0009.

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I review developments in theory and methodology that may allow us to begin creating innovative forms of organizing, rather than rest content with studying them after they have emerged. We now have the conceptual and technical apparatus to prototype organization designs at small scale, cheaply and fast. The process of organization re-design can be seen in terms of multiple stages. It begins with careful observation of phenomena. Qualitative or indeed quantitative induction (i.e. data mining) can play a critical role here. Once we have some understanding or at least conjectures about underlying mechanisms, we can use the behavioral lab or an agent-based model to run cheap experiments to adjust the design. Once we have formulated a new design, we may want to run a field experiment with randomization. If the results look satisfactory, we can scale up and implement.

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Book chapters on the topic "Agent-based data mining"

Baik, Sung Wook, Jerzy Bala, and Ju Sang Cho. "Agent Based Distributed Data Mining." In Parallel and Distributed Computing: Applications and Technologies, 42–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30501-9_11.

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Luo, Chao, Yanchang Zhao, Dan Luo, Chengqi Zhang, and Wei Cao. "Agent-Based Subspace Clustering." In Advances in Knowledge Discovery and Data Mining, 370–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20847-8_31.

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Arefin, Mohammad Shamsul, Rahma Bintey Mufiz Mukta, and Yasuhiko Morimoto. "Agent-Based Privacy Aware Feedback System." In Advanced Data Mining and Applications, 725–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14717-8_58.

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Zhang, Zili, and Chengqi Zhang. "8 Agent-Based Hybrid Intelligent System for Data Mining." In Agent-Based Hybrid Intelligent Systems, 127–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24623-7_8.

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Moemeng, Chayapol, Vladimir Gorodetsky, Ziye Zuo, Yong Yang, and Chengqi Zhang. "Agent-Based Distributed Data Mining: A Survey." In Data Mining and Multi-agent Integration, 47–58. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_3.

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Lee, HeungKyu. "Agent based Video Contents Identification and Data Mining Using Watermark based Filtering." In Data Mining and Multi-agent Integration, 315–24. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0522-2_22.

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Chaimontree, Santhana, Katie Atkinson, and Frans Coenen. "Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining." In Advances in Data Mining. Applications and Theoretical Aspects, 115–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14400-4_9.

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Hu, Wenbin, and Bo Meng. "Design and Implementation of Web Mining System Based on Multi-agent." In Advanced Data Mining and Applications, 491–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_59.

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Klusch, Matthias, Stefano Lodi, and Gianluca Moro. "Agent-Based Distributed Data Mining: The KDEC Scheme." In Intelligent Information Agents, 104–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36561-3_5.

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Lee, Raymond S. T., and James N. K. Liu. "iJADE eMiner - A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping." In Advances in Knowledge Discovery and Data Mining, 28–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45357-1_6.

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Conference papers on the topic "Agent-based data mining"

"Mobile Agent Based Distributed Data Mining." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.105.

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Bakar, Azuraliza Abu, Zulaiha Ali Othman, Abdul Razak Hamdan, Rozianiwati Yusof, and Ruhaizan Ismail. "Agent based data classification approach for data mining." In 2008 International Symposium on Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/itsim.2008.4631677.

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Xue, Zijun, Ting-Yu Ko, Neo Yuchen, Ming-Kuang Daniel Wu, and Chu-Cheng Hsieh. "Isa: Intuit Smart Agent, A Neural-Based Agent-Assist Chatbot." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00202.

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Klusch, Matthias, Stefano Lodi, and Gianluca Moro. "Issues of agent-based distributed data mining." In the second international joint conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/860575.860782.

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C., Nandini, and Rohini C. "Agent based Market Analysis using Data Mining." In International Conference on Computer Applications — Database Systems. Singapore: Research Publishing Services, 2010. http://dx.doi.org/10.3850/978-981-08-7300-4_0024.

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Bakar, Azuraliza Abu, Zulaiha Ali Othman, Abdul Razak Hamdan, Rozianiwati Yusof, and Ruhaizan Ismail. "An Agent Based Rough Classifier for Data Mining." In 2008 Eighth International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2008. http://dx.doi.org/10.1109/isda.2008.29.

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Han, Guilai, and Anquan Jiao. "Personalized Data Mining Based on Ontology and Agent." In 2010 WASE International Conference on Information Engineering (ICIE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icie.2010.109.

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Li, Xinyan, and Deren Li. "Agent-based modeling of urban land-use change." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651412.

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Moemeng, Chayapol, Xinhua Zhu, Longbing Cao, and Chen Jiahang. "i-Analyst: An Agent-Based Distributed Data Mining Platform." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.69.

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Liu, Xiong, Kaizhi Tang, John R. Buhrman, and Huaining Cheng. "An agent-based framework for collaborative data mining optimization." In 2010 International Symposium on Collaborative Technologies and Systems. IEEE, 2010. http://dx.doi.org/10.1109/cts.2010.5478500.

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