Menger’s theorem for fuzzy graphs
به نام خدا
Title: Menger’s theorem for fuzzy graphs
Authors: Sunil Mathew, MS Sunitha
Abstract: The concept of the strongest path plays a crucial role in fuzzy graph theory. In classical graph theory, all paths in a graph are strongest, with a strength value of one. In this article, we introduce Menger’s theorem for fuzzy graphs and discuss the concepts of strength- reducing sets and t-connected fuzzy graphs. We also characterize t-connected and t-arc connected fuzzy graphs.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: نظریه گراف (Graph Theory) – منطق فازی (Fuzzy Logic)
ایران سای – مرجع مقالات علمی فنی مهندسی
حامی دانش بومی ایرانیان
Data mining agent conversations: A qualitative approach to multi
به نام خدا
Title: Data mining agent conversations: A qualitative approach to multi-agent systems analysis
Authors: Emilio Serrano, Michael Rovatsos, Juan A Botia
Abstract: This paper presents a novel method for analyzing the behavior of multivalent systems on the basis of the semantically rich information provided by agent communication languages and interaction protocols specified at the knowledge level. More low-level communication mechanisms only allow for a quantitative analysis of the occurrence of message types, the frequency of message sequences, and the empirical distributions of parameter values. Quite differently, the semantics of languages and protocols in multi-agent systems can help to extract qualitative properties of observed conversations among agents. This can be achieved by interpreting the logical constraints associated with protocol execution paths or individual messages as the context of an observed interaction, and using them as features of learning samples. The contexts mined from such analyses, or context models, can then be used for various tasks, e.g. for predicting others future responses (useful when trying to make strategic communication decisions to achieve a particular outcome), to support ontological alignment (by comparing the properties of logical constraints attached to messages across participating agents), or to assess the trustworthiness of agents (by verifying the logical coherence of their behavior). This paper details a formal approach that describes our notion of context models in multi-agent conversations, an implementation of this approach in a practical tool for mining qualitative context models, and experimental results to illustrate its use and utility.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: داده کاوی (Data Mining) – عاملهای هوشمند (Intelligent Agents)
ایران سای – مرجع مقالات علمی فنی مهندسی
حامی دانش بومی ایرانیان
معرفی وب سایت ایران سای - مرجع علمی فنی مهندسی
به نام خدا
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ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
A survey of skyline processing in highly distributed environmen
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Title: A survey of skyline processing in highly distributed environments
Authors: Katja Hose Akrivi Vlachou
Abstract: During the last decades, data management and storage have become increasingly distributed. Advanced query operators, such as skyline queries, are necessary in order to help users to handle the huge amount of available data by identifying a set of interesting data objects. Skyline query processing in highly distributed environments poses inherent challenges and demands and requires non-traditional techniques due to the distribution of content and the lack of global knowledge. This paper surveys this interesting and still evolving research area, so that readers can easily obtain an overview of the state-of-the-art. We outline the objectives and the main principles that any distributed skyline approach have to fulfill, leading to useful guidelines for developing algorithms for distributed skyline processing. We review in detail existing approaches that are applicable for highly distributed environments, clarify the assumptions of each approach, and provide a comparative performance analysis. Moreover, we study the skyline variants each approach supports. Our analysis leads to taxonomy of existing approaches. Finally, we present interesting research topics on distributed skyline computation that have not yet been explored.
Publish Year: 2012
Published in: The VLDB Journal – Springer
موضوع: پردازش توزیع شده (Distributed Processing)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
Efficient stochastic algorithms for document clustering
به نام خدا
Title: Efficient stochastic algorithms for document clustering
Authors: Rana Forsati, Mehrdad Mahdav, Mehrnoush Shamsfard, Mohammad Reza Meybodi
Abstract: Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Addition- ally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document data- sets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: الگوریتمهای تکاملی (Evolutionary Algorithms)- (Stochastic Algorithms)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان