With the appearance of the issue of huge group decision-making and the status quo that group-aggregation decision is only effective for small-scale groups, this paper firstly designs an improved Minimum Fuzzy C-means ...
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ISBN:
(纸本)9781424415281
With the appearance of the issue of huge group decision-making and the status quo that group-aggregation decision is only effective for small-scale groups, this paper firstly designs an improved Minimum Fuzzy C-means (MFCM) based on Minimum Connected Dominating Set algorithm (MCDSA), and then, through defining the group-preference vector, group-consensus index and group decision value, the paper puts forward a new method to deal with huge group aggregation by making use of MFCM. As is shown by simulations and control analysis, the new method could effectively resolve the problem of making decisions for a group with a membership of over 600.
With increasing applications of RFID (Radio Frequency Identification) technology, reliability requirements for the deployment of RFID readers have become more critical. For a robust deployment of a RFID network, a sys...
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ISBN:
(纸本)9781424406012
With increasing applications of RFID (Radio Frequency Identification) technology, reliability requirements for the deployment of RFID readers have become more critical. For a robust deployment of a RFID network, a systematic solution was proposed in this paper. To simplify the reader deployment problem, instead of the traditional 2D circle-like shape assumption, the ellipse/ellipsoid-like shape was taken to represent the signal range of an RFID antenna in this paper. Further, the performance of RFID was estimated statistically. In addition, a deployment tool was designed for users to obtain a recommended layout. For a given area, solutions of number and placement of RFID readers are computed fast and robustly. To avoid reader collision which may result in low efficiency of a RFID network, two simplified mechanisms were proposed. Finally, the practical implementation and simulations were performed to test the practicability of the deployment tool and further to confirm the robustness and adequacy of the proposed models.
In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients w(l) , w(u) a...
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ISBN:
(纸本)9781424409723
In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the clusters. Then we point out its shortcoming in adjusting the three coefficients w(l) , w(u) and epsilon. To tackle this problem, a rough k-means clustering method is finally presented with adaptive parameters. This algorithm is used in a testing sample and obtains a less error clustering rate.
In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel b...
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In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.
Gather the information of the environment by the monocular vision. Using the H and S weight of the HSV color model, separate the target from the environment with a certain color, by a fast clustering algorithm for two...
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ISBN:
(纸本)9787811240559
Gather the information of the environment by the monocular vision. Using the H and S weight of the HSV color model, separate the target from the environment with a certain color, by a fast clustering algorithm for two-value image segmentation. Calculating the distance between the camera and target by the 3D reconstruction algorithm and sub-control strategy, and raise its veracity by laser information fusion. Furthermore, a vision servo system has been designed and utilized to achieve the robot's dynamic track. At last, some experiments were used to certification its availability.
Machine-learning research is to study and apply the computer modeling of learning processes in their multiple manifestations, which facilitate the development of intelligent system. In this paper, we have introduced a...
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Machine-learning research is to study and apply the computer modeling of learning processes in their multiple manifestations, which facilitate the development of intelligent system. In this paper, we have introduced a clustering based machine-learning algorithm called clustering algorithm system (CAS). The CAS algorithm is tested to evaluate its performance and find fruitful results. We have been presented some heuristics to facilitate machine-learning authors to boost up their research works. The InfoBase of the Ministry of Civil Services is used to analyze the CAS algorithm. The CAS algorithm is compared with other machine-learning algorithms like UNIMEM, COBWEB, and CLASSIT, and was found to have some strong points over them. The proposed algorithm combined advantages of two different approaches to machine learning. The first approach is learning from Examples, CAS supports Single and Multiple Inheritance and Exceptions. CAS also avoids probability assumptions which are well understood in concept formation. The second approach is learning by Observation. CAS applies a set of operators that have proven to be effective in conceptual clustering. We have shown how CAS builds and searches through a clusters hierarchy to incorporate or characterize an object. (c) 2006 Published by Elsevier B.V.
Extracting a smooth curve from unordered data has many applications to image analysis. However, many reported methods assume either that the shape of the input data is known a priori or that the boundary of the data i...
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Extracting a smooth curve from unordered data has many applications to image analysis. However, many reported methods assume either that the shape of the input data is known a priori or that the boundary of the data is clearly defined. We present a method that can handle several types of data sets. The main idea of the method is to extract a generalized curve, which passes through the data set. The proposed method is able to extract a smooth curve from complicated unordered pattern data and without any prior knowledge of the shape of the input data. Experimental results show that our method can produce good results for many data sets including handwritten Chinese characters. (c) 2006 Society of Photo-Optical Instrumentation Engineers.
This paper focuses on document clustering algorithms that build hierarchical solutions. In this paper is evaluate the performance of different criterion functions for the problem of clustering documents.
ISBN:
(纸本)9781424413218
This paper focuses on document clustering algorithms that build hierarchical solutions. In this paper is evaluate the performance of different criterion functions for the problem of clustering documents.
In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the *** we point out its shortcoming in adjusting thethree coefficients W1, Wu and ε.To tackle t...
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In this paper, we firstly analyze Lingras' algorithm with respect to its objective-function, numerical stability of the *** we point out its shortcoming in adjusting thethree coefficients W1, Wu and ε.To tackle this problem, arough k-means clustering method is finally presented with adaptive *** algorithm is used in a testing sample and obtains a less error clustering rate.
clustering is an important research topic and cure technology in Data Mining. clustering algorithms have been researched deeply. Now, there are lots of different clustering algorithms, these algorithms are used in spe...
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clustering is an important research topic and cure technology in Data Mining. clustering algorithms have been researched deeply. Now, there are lots of different clustering algorithms, these algorithms are used in special fields and users. In order to use these algorithms better, some researchers have inferred some standards to evaluate the clustering algorithms. This paper aims to evaluate clustering algorithms form another aspect-using the overlap rate between clusters to compare clustering algorithms. Based on the concept of overlap rate, we can generate data sets with controlled the overlap rate between clusters and the geometrical character. Then we use the data set to evaluate clustering algorithms to find the applicability of clustering algorithms.
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