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In the literature on subspace clustering, traditional clustering techniques have been extended for computing meaningful and interesting clusters in the appropriate subspaces of the high dimensional data. We present a novel algorithm to capture unobserved object relationships embedded in fuzzy subspaces. In order to model the uncertainties of fuzzy data, we propose a modification of fuzzy c-means algorithm...
In the recent years, major CPU designers have shifted from ramping up clock speeds to add on-chip multi-core processors. Algorithms and applications must be tuned to allow multi-core processors to exploit their inherent parallelism. An experiment is carried out with data mining (DM) algorithms, to explore the potential of quad-core hardware architecture with OpenMP API (application programming interface)...
Based on the basic theory of the representative artificial immune network model, a hierarchical clustering algorithm aiNHCA was presented. Firstly, aiNHCA generates the memory matrix and the similarity matrix of the antibodies in the aiNet method. So it can divide the data set into several sub-clusters. Then it combines the sub-clusters with higher similarities in the hierarchical clustering method...
In this paper we present a new clustering method based on K-means that have avoided alternative randomness of initial center. This paper focused on K-means algorithm to the initial value of the dependence of K selected from the aspects of the algorithm is improved. First, the initial clustering number is radicN. Second, through the application of the sub-merger strategy the categories were combined...
Evaluation of water environment quality plays an important role in environment science. Because of many factors affecting environment quality, it is a basic task to select rational pattern and make full use of limited information from monitors so as to describe environment quality objectively. In view of the deficiency of the traditional methods, based on the grey theory, a grey clustering model is...
Evolutionarily stable strategies (ESS) is used between species co-exist in small medium enterprise (SME) clusters. We assume that each of the potential helpers knows the otherspsila strategies in cluster in this paper. We show that the ability to observe their realizations influences the evolutionarily stable strategies (ESS) of the game. According to our results, under the partial information structure...
The research aims at establishing system of urban land grade and datum land prices in Hubei province of China. The balance of urban datum land prices is made to harmonize regional land prices among cities and to make datum land prices truly reflect different economic development levels. Principal component analysis enhances selection of evaluation factors and confirmation of weights. K-Means clustering...
This paper presents a novel content-based hidden transmission method for secret data to improve the security and secrecy. In the proposed method, the secret data is encrypted by chaotic map before embedding. Then the cover image is segmented by watershed algorithm and fuzzy c-means clustering. At last we extract the feature of each region and embed the secret data into the cover image according to...
This paper introduces a non-temporal multiple silhouettes in Hidden Markov Model (HMM) for offering view independent human posture recognition. The multiple silhouettes are used to reduce the ambiguity problem of posture recognition. A simple feature extraction of the 2D shape contour based histogram is used for image encoding and K-Means algorithm is applied for clustering and code-wording of eight...
Density-based clustering and density-based outlier detection have been extensively studied in the data mining. However, Existing works address density-based clustering or density-based outlier detection solely. But for many scenarios, it is more meaningful to unify density-based clustering and outlier detection when both the clustering and outlier detection results are needed simultaneously. In this...
Clustering is popular used in customer value segmentation in business research. Compared with other clustering methods, the objective clustering analysis can automatically and objectively determine the number of clusters and find out the optimal clustering scheme. This investigation discussed the reasonable evaluation system of value-driven customer segmentation, identified customer behavior using...
Fuzzy neural network can handle non-linear, complex data, but the structure of model determination is an important and difficult issues identified. More complete results can be made in a short period of time by the optimization network model. To address this issue, this paper presents the fusion of a quantum clustering algorithm and fuzzy c-means clustering algorithm, the fuzzy neural network structure...
Clustering is used to find out the objects that resemble each other and compose different groups, cluster analysis is an important job in data mining. This article brings the rough set into fuzzy cluster, by using the methods of attributes contracted in the rough set theory to improve the FCM algorithm; the improved algorithm had been proved a high precise ratio.
Solving mathematical problems is both challenging and difficult for many students. This paper proposes a document retrieval approach to help solve mathematical problems. The proposed approach is based on Kohonenpsilas Self-Organizing Maps for data clustering of similar mathematical documents from a mathematical document database. Based on a user query problem, similar mathematical documents with their...
Text clustering is an important task of text mining. The purpose of text clustering is grouping similar text documents together efficiently to meet human interests in information searching and understanding. The procedure of clustering should involve a cognitive process of text understanding or comprehension.This paper introduces an innovative research effort, CogHTC, a hierarchical text clustering...
Since the reform and opening-up policy, China's urbanization has improved continuously. Even though, here comes the bottleneck following the unplanned construction. In the face of obstacles, how to overcome these difficulties and by which means can we improve the level of urbanization comprehensively has become popular in the academic community. Whereas the situation mentioned above, this paper is...
In complex process of industrial production, it need deal with a large number of data, multiple dimensions, and generate complex data. If the neural network control indirect used, it is easy that lead to some shortcomings, such as inaccurate results and training stage of neural network lack convergence and so forth. In response to these circumstances, the integration model of data optimize processing...
The basic K-means is sensitive to the initial centre and easy to get stuck at local optimal value. To solve such problems, a new clustering algorithm is proposed based on simulated annealing. The algorithm views the clustering as optimization problem, the bisecting K-means splits the dataset into k clusters at first, and then run simulated annealing algorithm using the sum of distances between each...
To locate the object accurately in a scene for further vision processing, a novel approach for figure-ground segmentation is proposed, which combines the normalized-cut method (Ncut) and top-down method inspired by the trickle-up and trickle-down processing in primate visual pathways. Firstly, as the trickle-up stage, the Ncut method groups the pixels into multiple partitions based on the global criterion,...
A wireless sensor network has potential to monitor stimulus around it. Sensor networks have severe energy constraints, low data rate with high redundancy, and many-to-one flows. Thus, data centric mechanisms that perform in-network aggregation of data are needed. Clustering is one of the data centric mechanisms in which various cluster heads perform in-network aggregation of data. Thus, there is more...
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