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How to improve the efficiency of discovering the frequent item sets is a major problem in mining association rules. This paper analysised the idea and performance of the general quantitative association rules algorithm ,and put forward a quantitative association rules mining algorithm based on matrix, the new algorithm firstly transformed quantitative database into Boolean matrix ,then used boolean...
Rough set (RS) and support vector machine(SVM) have gradually been becoming hot spots in the territory of artificial intelligence, machine learning and data mining research. In this paper, RS and SVM theories have been discussed, a new hybrid RS-SVM model was proposed based on the attribute reduction of RS and the classification principles of SVM, which has been analyzed its possibility of application...
One critical issue in wireless sensor networks is how to gather sensed information in an energy-efficient way since the energy is a scarce resource in a sensor node. In this paper, we propose an improved distributed clustering algorithm, which is called LEACH-W algorithm, In this algorithm, we extend low-energy adaptive clustering cluster-head selection algorithm by a factor with weight value deterministic...
This paper proposes an energy efficient clustering formation algorithm for wireless sensor network, which is called ACE-CILP algorithm according to considering the energy consumption as an optimization parameter. The algorithm can divide a sensor network into a few clusters and select a cluster head base on weight value that leads to more uniform energy dissipation evenly among all sensor nodes. The...
One important critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is a scarce resource in a sensor node. Clustering technique has been proven to be an effective approach for data-gathering in wireless sensor networks. However, these data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. Data...
In this paper, we propose an algorithm that can be efficiently used to search through scale-free networks. The algorithm uses local information such as the identities and connectedness of a nodepsilas neighbours, and its neighbours, but not the targetpsilas global position. We demonstrate that our search algorithm work well on a simulative networks, scale with the number of nodes, and may help reduce...
Data Mining is rapidly evolving areas of research that are at the intersection of several disciplines, including statistics, databases, pattern recognition, and high- performance and parallel computing. In this paper, we propose a novel mining algorithm, called ARMAGA (association rules mining algorithm based on a novel genetic algorithm), to mine the association rules from an image database, where...
Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Mining encompasses various algorithms such as clustering, classification, and association rule mining. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(association...
Grid computing is a new computing-framework to meet the growing computational demands. Grid computing provides mechanisms for sharing and accessing large and heterogeneous collections of remote resources. However, how to scheduling the subtasks in these heterogeneous resources is a critical problem. This paper puts forward a task scheduling algorithm based on genetic algorithm. It first generates...
The existing globus grid environment emphasizes resources searching and marking-up, and is not so perfect in task submission and ways of scheduling, in which the way of task submission is manual; task scheduling depends on round robin method. To research into the task scheduling in grid computing, this paper puts forward a model dealing with task scheduling, designs and realizes a hybrid genetic algorithm...
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