Integration of Data Mining with Game Theory

Wang, Yi

doi:10.1007/0-387-34403-9_38

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Recently, this kind of view or to be more precise, this type of mathematical modeling based on game theory has become very popular in data mining, so that research on new methods in data clustering, data classification, data pattern extraction, or data prediction models has been extended [11]. For the first time in his research, Stahl proposed a method based on game theory and Nash learning for data prediction learning rules [36], [48]. His method was based on logical behavior which has a probabilistic basis.…”

Section: Methodsmentioning

confidence: 99%

Using BTA Algorithm for Finding Nash Equilibrium Problem Aiming the Extraction of Rules in Rule Learning

Boudaghi

Mahan

Isazadeh

2021

Preprint

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It is crystal clear that discovering the rules for finding a specific pattern among given data for extraction of association rules and rule-based learning systems has been defined. There is much research that make use of game theory for the processes contributing to discovery of rules. In recent years, modeling based on game theory in rule learning sphere has gained much more attention by computer scientists. When there is independence between the strategies used by two or more different players the strategy game modeling could be used. In this view, strategic play is a desirable model for situations with no permanent strategic relationship among interactions. In addition, Nash equilibrium is the most widely used solution concept in game theory. This concept is a state-of-the-art interpretation of a strategy game. Each player has an accurate prediction of other players' behavior and acts according to such a rational prediction. In the present study, by extracting rules from frequent patterns we have presented a model that can extract learning rules by abstraction based on game theory, which can be used not only for association rules but also for rule-based learning systems. Also, the introduced method can be easily generalized to fuzzy data. To Find Nash Equilibrium (FNE) in the proposed method, we used meta-heuristic Bus Transportation Algorithm (BTA).The results indicated that the method reduces computational complexity in the associate rule discovery process and rule learning, provided that FNE is solved.

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Section: Methodsmentioning

confidence: 99%

Using BTA Algorithm for Finding Nash Equilibrium Problem Aiming the Extraction of Rules in Rule Learning

Boudaghi

Mahan

Isazadeh

2021

Preprint

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“…For example if the peer is interested in predicting the chance of downloading without any interruption, he might simulate the whole transaction exactly with the history of the opponent. [5] Has effectively used the prediction technique to identify a winning strategy using data mining. We can use the same [6] Game theoretic technique to identify the malicious peer using data mining and Nash equilibrium concept.…”

Section: E Predictionmentioning

confidence: 99%

Controlling Free Riders in Peer to Peer Networks by Intelligent Mining

Ganesh¹,

R²

2009

IJCEE

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Abstract-In a typical Peer to Peer system, each node is owned and operated by an independent entity, and the nodes collectively form a self-organizing, self-maintaining network with no central authority. The peer to peer Technology has attracted a significant amount of interest because of its capacity for resource sharing and content distribution. The existence of high degree of free riding is a serious threat to P2P network. This paper deals with the issue of identifying free riders and other kind of peers who subvert the system. Additionally, this paper also proposes a basic data warehouse structure for a peer to peer system and analyzes peer behavior using data mining functionalities. Results prove that the free riders and malicious peers get automatically eliminated from the system.

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“…The dynamic causal mining (DCM) algorithm (Pham et al, 2005) is based on the intelligent counting algorithm. It was extended (Pham et al, 2006) with delay and feedback analysis, and was further improved for the analysis in game theory with formal concept analysis (Wang, 2007). DCM enables the generation of dynamic causal rules from data sets by integrating the concepts of systems thinking (Senge et al, 1994) and system dynamics (Forrester, 1961) with association mining (Agrawal et al, 1996).…”

Section: Literature Reviewmentioning

confidence: 99%

Applying intelligent cultural networks to marketing analysis

Wang

2009

International Marketing Review

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -In the real world, an occurrence of an event is often affected by a large number of potential factors. The purpose of this paper is to identify causal factors hidden in the data and discover the underlying causality from the observed data. Design/methodology/approach -This paper suggests an integration of system dynamics and association mining for identifying causality between attributes in a cultural analysis. The framework gives an improved description of the target cultural system represented by a database; it can also improve strategy selection and other forms of decision making. Such a combination extracts important dynamic causality. Findings -Complicated cultural issues can be identified and managed through a causal relation network. This type of causal relation is very common in daily life. For example, "an increase in productivity in a factory might cause an increase in pollution in the environment" and "the increasing pollution will cause a decreasing level of human health and welfare". Practical implications -This paper presents a methodological framework for studying, understanding and managing cultural differences in a marketing environment. This framework provides a foundation for characterizing the causality representations and relations distributed among members of cultural groups. Originality/value -This framework is being developed as an approach to improve the management of a dynamic environment, such as an international marketing environment, where participants (marketers, sales manager, etc.) are asked to communicate, bargain, analyse and collaborate with other participants who have a different cultural background or understanding. The knowledge employed can be extracted from data gathered from previous cases, from which the models can be developed.

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Integration of Data Mining with Game Theory

Cited by 10 publications

References 5 publications

Using BTA Algorithm for Finding Nash Equilibrium Problem Aiming the Extraction of Rules in Rule Learning

Using BTA Algorithm for Finding Nash Equilibrium Problem Aiming the Extraction of Rules in Rule Learning

Controlling Free Riders in Peer to Peer Networks by Intelligent Mining

Applying intelligent cultural networks to marketing analysis

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