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In many telecom and web applications, there is a need to identify whether data objects in the same source or different sources represent the same entity in the real-world. This problem arises for subscribers in multiple services, customers in supply chain management, and users in social networks when there lacks a unique identifier across multiple data sources to represent a real-world entity. Entity...
In order to improve detection efficiency of on-line web news stream, we propose a new method to accomplish detection task with window-adding, named entity recognition and suffix tree clustering. In our method, we make full use of informative elements of news stream(such as date, place, person and so on) to help detection process, and this method decreases text similarity computation greatly. Experimental...
Readers in radio frequency identification systems are in dire need of an anti-collision algorithm for identifying tags in the interrogation zone by consuming the most minimum time. Electing a good anti collision algorithm, in general is made based on the deployment area. Existing algorithms suggested in the last 3 decades are currently domain specific. Many algorithms produce remarkable results in...
This paper studies and analyses the problems and deficiencies that are faced by clustering algorithm which is based on hierarchical scene tree, and proposes a clustering algorithm that is based on the best-first search strategy. The algorithm reduces the false-matching rate, and also alleviates the problem of network congestion to some extent, by designing a novel cost function. Both theoretical analysis...
Traditional Clustering is a powerful technique for revealing the "hot" topics among documents. However, it's hard to discover the new type events coming out gradually. In this paper, we propose a novel model for detecting new clusters from time-streaming documents. It consists of three parts: the cluster definition based on Multi-Representation Index Tree (MI-Tree), the new cluster detecting...
The growing self-organizing map (GSOM) is a variation of the popular self-organizing map (SOM). It was developed to address the issue of identifying a suitable size of the SOM, which is usually concerned with vectorial items. To deal with algoritms implemented as programs, which are hardly represented by vectors, a new version of GSOM for clustering non-vectorial items (GSOM/NV) is proposed here....
Intrusion detectors isolate intrusions based on allowable and disallowable activities. The disallowable policy enforcers will alert only on events that are known to be bad while the allowable policy enforcer will alert on events that deviate from those that have been classified as good. However, these trade-offs become difficult to balance in a recent time due to the complexity of computer attacks...
Initialization of fuzzy k-means algorithm decreases the convergent rate of clustering and leads to plenty of calculation. Thus, we propose an improved fuzzy k-means clustering based on k-center algorithm and binary tree in this paper, which firstly reduces redundant attributes while too many irrespective attributes affect the efficiency of clustering. Secondly, we remove the differences of units of...
An algorithm, TBCClustering, is presented in the paper for clustering GML documents using maximal frequent induced subtree patterns. TBCClustering mines the maximal frequent induced subtrees by using the structural information of GML documents, it can get the best minimum support automatically, and then chooses a set of subtree patterns to form the optimistic clustering features. Finally it uses CLOPE...
Many existing clustering algorithms use a single prototype to represent a cluster. However sometimes it is very difficult to find a suitable prototype for representing a cluster with an arbitrary shape. One possible solution is to employ multi-prototype instead. In this paper, we propose a minimum spanning tree (MST) based multi-prototype clustering algorithm. It is a split and merge scheme. In the...
This paper proposed a new point symmetry-based ant clustering algorithm which can defect the number of clusters and the proper partitions from data sets when data sets possess the property of symmetry. In the proposed algorithm, a revised ant clustering algorithm is presented which can reduce the running time of standard ant clustering algorithm. Each ant represents a data object. It will decide its...
An Iterative Clustering Steiner Tree (ICST) algorithm is proposed to connect hose based VPN endpoints using a shared tree for resource optimization. Simulation results show the ICST algorithm can achieve better performance on resource utilization.
Two trends in clustering (also called unsupervised classification) problem: from one-way to two-way and from tree structure to net structure, are integrated in this paper to a framework of two-way combinatorial clustering network (TWCCN). The theory of directed branch-connected tree (DBCT) is constructed to describe the model of TWCCN, and algorithms based on nonnegative matrix factorization (NMF)...
In this paper, problem of efficient representation of large database of target radar cross section is investigated in order to minimize memory requirements and recognition search time, using a tree structured hierarchical wavelet representation. Synthetic RCS of large aircrafts, in the HF-VHF bands, are used as experimental data. Hierarchical trees are built using wavelet multiresolution representation...
In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree. Eventually, we can get k clusters from the root of the CF-Tree. This...
Divide-and-Merge is a methodology for clustering a set of objects that combines a top-down "divide" method with a bottom-up "merge" method. In this paper, we propose a 2-way normalized cut with automatically determining K clustering algorithm (BNAK-Divide-and-Merge) based on the Divide-and-Merge. In order to improve the efficiency and performance of the divide phase, our methodology...
An adaptive parallel algorithm for hierarchical clustering based on PRAM model was presented. Performing the data preprocessing depended on ldquo90-10rdquo rule to decrease the numbers of data set, performing the parallel algorithm for creating Euclid Minimum Spanning Trees on absolute graph, performing the algorithm for finding the disjoining strategies and non-collision memory, data set was clustered...
Firstly, the paper makes a briefly analysis and comment about the fuzzy c-means clustering algorithm. Then a new kind of hybrid genetic algorithm is proposed on the base of the combination of genetic algorithm and simulated annealing algorithm, and it is applied in fuzzy c-means clustering. It overcomes the locality and the Sensitivity to initial clustering central of fuzzy c-means clustering, by...
The k-means algorithm is an extremely popular technique for clustering data. One of the major limitations of the k-means is that the time to cluster a given dataset D is linear in the number of clusters, k. In this paper, we employ height balanced trees to address this issue. Specifically, we make two major contributions, (a) we propose an algorithm, RACK (acronym for RApid Clustering using k-means),...
The high score pigtail rate satellite remote sensing image may provide the rich data for the urban quakeproof disaster reduction information system's construction. But, because its slight information is specially rich, has brought certain difficulty for the related terrain feature object detection and the extraction. The image division's goal lies in the original image divides some in the space neighboring,...
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