We study the problem of identifying Web communities around some seed vertex. In this work, we propose a fast graph algorithm to expand Web communities in a scalable style. Given a seed vertex, our algorithm computes a...
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We study the problem of identifying Web communities around some seed vertex. In this work, we propose a fast graph algorithm to expand Web communities in a scalable style. Given a seed vertex, our algorithm computes approximate personalized PageRank vectors with better and better approximations, and finds the smallest conductance sets on these vectors as candidate communities in nearly-linear time. At the end, it returns the candidate community with the smallest conductance as the result community. We also define local community profile (LCP) to investigate structural and statistical properties of Web communities in a local range. Theoretical analysis and primary experiments both show the efficiency of the proposed algorithm and the quality of the results.
Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender ...
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Collaborative filtering (CF) techniques have achieved widespread success in E-commerce nowadays. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the improvement of scalability and efficiency of collaborative filtering (CF) algorithms become increasingly important and difficult. In this paper, we developed and implemented a scaling-up item-based collaborative filtering algorithm on MapReduce, by splitting the three most costly computations in the proposed algorithm into four Map-Reduce phases, each of which can be independently executed on different nodes in parallel. We also proposed efficient partition strategies not only to enable the parallel computation in each Map-Reduce phase but also to maximize data locality to minimize the communication cost. Experimental results effectively showed the good performance in scalability and efficiency of the item-based CF algorithm on a Hadoop cluster.
In this paper, we present a novel Differential Evolution (DE) algorithm to solve high-dimensional global optimization problems effectively. The proposed approach, called DEVP, employs a variable population size mechan...
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In this paper, we present a novel Differential Evolution (DE) algorithm to solve high-dimensional global optimization problems effectively. The proposed approach, called DEVP, employs a variable population size mechanism, which adjusts population size adaptively. Experiments are conducted to verify the performance of DEVP on 19 high-dimensional global optimization problems with dimensions 50, 100, 200, 500 and 1000. The simulation results show that DEVP out performs classical DE, CHC (Crossgenerational elitist selection, Heterogeneous recombination, and Cataclysmic mutation), G CMA-ES (Restart Covariant Matrix Evolutionary Strategy) and GODE (Generalized Opposition-Based DE) on the majority of test problems.
Mining frequent closed itemsets in data streams is an important task in stream data mining. In this paper, an efficient mining algorithm (denoted as EMAFCI) for frequent closed itemsets in data stream is proposed. The...
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This paper proposes a simple object extraction and recognition method with efficient searching for identifying and extracting the objects in a complex scene based on the color features. The background of the images is...
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This paper proposes a simple object extraction and recognition method with efficient searching for identifying and extracting the objects in a complex scene based on the color features. The background of the images is needed to be extracted and recognized in order to get the object of the interested in the images first. This can be achieved by getting the best separation line between building and road, followed by the interested objects (vehicles) on the road. The vehicle objects are represented by using Minimum Bound Rectangle (MBR) and the vehicle object representative points will be the left bottom coordinate of the MBR. The color of the vehicles will be used as the attributes of the objects. Experiments have been conducted to demonstrate that single and multiple known objects in complex scenes can be extracted by using this approach.
Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated...
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Machine learning algorithms, which have been considered as robust methods in different computational fields, assume that the training and test data are drawn from the same distribution. This assumption may be violated in many real world applications like bank failure prediction because training and test data may come from different time periods or domains. An efficient novel algorithm known as Fuzzy Refinement (FR) is proposed in this paper to solve this problem and improve the performance. The algorithm utilizes the fuzzy system and similarity concept to modify the instances' labels in target domain which was initially predicted by shift-unaware Fuzzy Neural Network (FNN) proposed by [1]. The experiments are performed using bank failure financial data of United States to evaluate the algorithm performance. The results address a significant improvement in the predictive accuracy of FNN due to applying the proposed algorithm.
This paper proposes a model-based decision support tool using fuzzy optimization for assessing regional sustainability development (RSD) under climate change. The proposed tool integrates fuzzy goal programming (FGP) ...
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More and more clustering approaches are used in educational data mining for too many data are generated by web-based educational systems. To deal with the problem of premature convergence of the traditional K-means al...
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More and more clustering approaches are used in educational data mining for too many data are generated by web-based educational systems. To deal with the problem of premature convergence of the traditional K-means algorithm and computational costs, an improved K-means clustering algorithm based cooperative PSO frame is proposed in this paper. Cooperative PSO has been proved effective for large scale and complex problems via a divide-and-conquer strategy by simulating coevolutionary techniques in nature. Therefore, K-means clustering algorithm based cooperative PSO frame is effective in educational data clustering.
Using an appropriate leadership style is essential to improve the organizational *** paper presents a novel transformational leadership model to conquer the organizational ambiguities and *** proposed approach is call...
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Using an appropriate leadership style is essential to improve the organizational *** paper presents a novel transformational leadership model to conquer the organizational ambiguities and *** proposed approach is called Respect Oriented Leadership Model with the core of respect *** means considering the idea and opinion of each expert in leading *** the meaning of respect concept that is the kernel of the proposed Model (ROLM),RESPECT is standing for Refer to Experts’ Significant Perspectives in Each Conduction *** ROLM participating in decision,making process is duty of related expert persons in corresponded *** model components support the change driving process.
Learning Object (LO) is one of the main research topics in the e-learning community in the recent years, and most researchers pay attention to the issue of Learning Object Reusability. The most obvious motivation is t...
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Learning Object (LO) is one of the main research topics in the e-learning community in the recent years, and most researchers pay attention to the issue of Learning Object Reusability. The most obvious motivation is the economic interest of reusing learning material instead of repeatedly authoring it. In this paper we present the process of creating granular learning object for enabling the reusing of LO in e-Learning context. Reusability requires the LO to be in a fine-grain form because raw media elements are often much easier to reuse then aggregate assemblies. In other words, as the LO size decreases (lower granularity), its potential for reuse increases. A prototype of learning objects repository that contain granular LO has been developed to show the process of granularizing LO. Each of existing LO will be extracted into granular element with the metadata and the system generated unique id for each LO based on its identifier (URL of LO) and save automatically the metadata contain in the LO. These metadata will be used for searching and retrieval process of the granule LO.
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