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Wireless Nano-Sensor networks (WNSN) consist of nanosensors equipped with nanotransceivers and nanoantennas to operate in Terahertz frequency band (0.1–10THz). Due to the peculiarities of this communication channel (such as, very short range of transmission (under lm), high interference, high path loss) and limited capabilities of nano-nodes (such as, computing, sensing, memory, energy), the existing...
This paper describes a method for distribution of people monitoring system using a 3D camera. This camera measures distance and intensity distribution based on time of flight (TOF). From the obtained distance data, we perform clustering processing to detect the distribution of people. In our method, fuzzy logic plays a primary role to decide the cluster, i.e., people number. Our proposed method applied...
Chiu proposed a clustering algorithm adjusting the numeric feature weights automatically for k-anonymity implementation and this approach gave a better clustering quality over the traditional generalization and suppression methods. In this paper, we propose an improved weighted-feature clustering algorithm which takes the weight of categorical attributes and the thesis of optimal k-partition into...
Association rule mining is a well-known data mining task for discovering association rules between items in a dataset. It has been successfully applied to different domains especially for business applications. However, the mined rules rely heavily on human interpretation in order to infer their semantic meanings. In this paper, we mine a new kind of association rules, called conceptual association...
13 lines including 9 wheat mutants of genetic transformation by ion beam and 4 acceptor control varieties were analyzed by SSR marker. A special band of dwarf mutant line 1042 was obtained, sequenced and analyzed by blast software. The genetic distance was analyzed with SSR marker materials by SAS software. The result showed that 9 wheat mutants were clustered together with their own acceptor control...
The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
Clustering sensor nodes is an effective technique to prolong the lifetime of wireless sensor networks. In this paper we investigate the impact of heterogeneity of nodes, in terms of their energy and data amount, and propose a novel adaptive, distributed, energy efficient clustering algorithm AEEC for wireless sensor networks, which better suits the heterogeneous sensor networks. Our approach elects...
Wireless sensor network consisting of a large number of sensors is effective for gathering data in a variety of environments. Since the sensors operate on battery of limited power, it is a challenging task to design an efficient routing scheme which can minimize the delay while offering high energy efficiency and long network lifetime. In this paper we propose a new routing protocol and data gathering...
In this study, a novel iterative optimization clustering algorithm is proposed by using a manifold distance based dissimilarity metric which can measure the geodesic distance along the manifold and a criterion function which can express the clustering target, that is the samples in the same cluster being somehow more similar than samples in different one. The steps of the algorithm are discussed in...
A novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-DTA) for solving multiclass problems is proposed in this paper. A clustering algorithm was used to determine the hierarchy of binary decision subtasks performed by the SVM binary classifiers. The applied clustering model utilizes Mahalanobis distance measures at the kernel space for better consistency with...
In this paper, we propose a new ant based clustering algorithm. The algorithm takes inspiration from the sound communication properties of real ants. Artificial ants communicate directly with each others in order to merge similar group of objects. The proposed algorithm was tested and evaluated. The obtained results are very encouraging in comparison with the famous k-means and some ant based clustering...
Research in sensor networks has focused on development of energy efficient infrastructures. In this article, we introduce a new approach to organize sensor networks in clusters in order to reduce energy dissipation. Our contribution is an heuristic to define the number of clusters and also an efficient manner to choose cluster heads by minimizing the distance between cluster heads and its cluster...
This paper describes an evolutionary clustering algorithm, which can partition a given dataset automatically into the optimal number of groups through one shot of optimization. The proposed method is based on an evolutionary computing technique known as the Bacterial Evolutionary Algorithm (BEA). The BEA draws inspiration from a biological phenomenon of microbial evolution. Unlike the conventional...
The Kohonen self organizing map (SOM) is an excellent tool in exploratory phase of data mining. The SOM is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units needs to be grouped i.e., clustered...
For solving the incomplete data problem of missing feature values in prototype data, various strategies were proposed. In this paper, two improved approaches are proposed to estimate the missing values of incomplete data. The two approaches are based on combining the adaptive volume Gustafson-Kessel algorithm (GKA) and the nearest vector features under the distance norm evaluated by complete data...
Spatial data mining (SDM) is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. Being an important role of SDM, spatial clustering is to organize a set of spatial objects into groups (or clusters) such that objects in the same group are similar to each other and different from those in other groups. Spatial clustering has been...
At present, several algorithms have been proposed to extend the network lifetime for Wireless Sensor Network (WSN). However, these algorithms did not achieve the desired results in balancing the energy consumption among the nodes. To deal with this problem, an Energy Balancing Clustering Algorithm (EBCA) is proposed in this paper. The algorithm divides the whole WSN into balanced grids, where cluster...
Today in many domains there are very limited explicit ontologies established for building information systems. The information systems have only schemas for their information repositories which to some extent imply the semantics of the information. Traditional ontology-driven semantic integration approaches cannot be directly applied in integrating these information systems. In our work we use the...
This paper studies how to exploit spatial data correlations to group sensor nodes into clusters of high data aggregation efficiency. The approach proposed in this paper first selects a set of cluster heads that form a dominating set. Then a set of nodes selected by ant-colony algorithm are added to the above dominating set to make all the nodes in the set connected. Simulation results demonstrate...
We present a method to group trajectories of moving objects extracted from real-world surveillance videos. The trajectories are first mapped into a low dimensionality feature space generated through linear regression. Next the regression coefficients are clustered by a Gaussian mixture model initialized by K-means for improved efficiency. The model selection problem is solved with Bayesian information...
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