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We present a graph-based iterative algorithm for clustering task. The existing literatures in this domain often use the distance measure between the testing data point individual which is proved not enough in the real applications. In this paper, we think about the core concept in semi-supervised learning method, and use a graph to reflect the original distance measure, and combine the density information...
A new clustering route algorithm of p2p networks based on knodel graph is proposed in this paper. According to the primary method, our algorithm added physical clustering in ordering to create relation between node's physical position and logical id number can improve the route efficiency of the whole knodel p2p network.
Currently those algorithms to mine the alarm association rules are limited to the minimal support, so that they can only obtain the association rules among the frequently occurring alarms. This paper proposes a new mining algorithm based on spectral graph theory. The algorithms firstly sets up alarm association model with time series; Secondly, it regards alarms database as a high-dimensional structure...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
In this paper we have developed a connectivity based cluster validity index. This validity index is able to detect the number of clusters automatically from data sets having well separated clusters of any shape, size or convexity. The proposed cluster validity index, connect-index, uses the concept of relative neighborhood graph for measuring the amount of "connectedness" of a particular...
High-dimensional data presents a significant challenge to a broad spectrum of pattern recognition and machine-learning applications. Dimensionality reduction (DR) methods serve to remove unwanted variance and make such problems tractable. Several nonlinear DR methods, such as the well known ISOMAP algorithm, rely on a neighborhood graph to compute geodesic distances between data points. These graphs...
Most research on Internet topology is based on active measurement methods. A major difficulty in using these tools is that one comes across many unresponsive routers. Different methods of dealing with these anonymous nodes to preserve the connectivity of the real graph have been suggested. One of the more practical approaches involves using a placeholder for each unknown, resulting in multiple copies...
Cut detection is part of the video segmentation problem, and consists in the identification of the boundary between consecutive shots. In this case, when two consecutive frames are similar, they are considered to be in the same shot. This work presents an approach to cut detection using a rotation and translation invariant algorithm based on the use of the maximum cardinality of a bipartite graph...
Rare category detection is the task of identifying examples from rare classes in an unlabeled data set. It is an open challenge in machine learning and plays key roles in real applications such as financial fraud detection, network intrusion detection, astronomy, spam image detection, etc. In this paper, we develop a new graph-based method for rare category detection named GRADE. It makes use of the...
We introduce a class of geodesic distances and extend the K-means clustering algorithm to employ this distance metric. Empirically, we demonstrate that our geodesic K-means algorithm exhibits several desirable characteristics missing in the classical K-means. These include adjusting to varying densities of clusters, high levels of resistance to outliers, and handling clusters that are not linearly...
In this work we present a novel method to model instance-level constraints within a clustering algorithm. Thereby, both similarity and dissimilarity constraints can be used coevally. The proposed extension is based on a distance transformation by shortest path computations in a constraint graph. With a new technique cannot-links are consistently supported and the dissimilarity is extended to their...
We propose a new cluster validity index. A data partition is described by a set of disjoint sub-graphs, each corresponding to the minimum spanning tree of a cluster, taking as edge weight the dissimilarity between linked objects. Based on the assumption that each cluster has a characteristic parametric distribution of dissimilarity increments, graph probabilities are estimated. The validity index...
Clustering sensors nodes as the basic of routing is an efficient mechanism for prolonging the lifetime of wireless sensor networks. In this paper, the high-efficient multilevel clustering is abstracted as a root tree which has the performances of the minimal relay set and the maximal weight according to graph theory. A mathematical model for the clustering virtual backbone is built. Based on the model,...
The video segmentation problem consists in the identification of the boundary between consecutive shots. When two consecutive frames are similar, they are considered to be in the same shot. In this work, we use the maximum cardinality of the bipartite graph matching between two frames as the dissimilarity distance in order to identify the cut locations. Thus, if two frames are similar then the maximum...
Recently, spectral clustering has become one of the most popular modern clustering algorithms which are mainly applied to image segmentation. In this paper, we propose a new spectral clustering algorithm and attempt to use it for outlier detection in dataset. Our algorithm takes the number of neighborhoods shared by the objects as the similarity measure to construct a spectral graph. It can help to...
As a fundamental problem in data mining, pattern recognition and machine learning, clustering algorithm has been studied for decades, and has been improved in many aspects. However, parameter-free clustering algorithms are still quite weak, which makes their potential generalization to a lot of promising applications rather difficult. A parameter-free clustering algorithm based on density model is...
Clustering is the unsupervised classification of patterns into groups. It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. In this paper, the Hartuv and Shamirpsilas clustering algorithm for similarity graph is extended to the weighted similarity graph. The algorithm has the advantage of many existing algorithm:...
This paper considers the multicriteria route selection problem (mRSP) for car navigation systems in traffic road network. A multilayer hierarchy network method is proposed to substantially reduce the computation time when solving mRSP in big scale road network. In our proposed hierarchical method, an efficient genetic-based cluster method is used to overcome the size limitations with acceptable loss...
A method of similarity clusters detection in large visual databases is described in this work. Similarity clusters have been defined on the basis of a general concept of similarity measure. The method is based also on the properties of morphological spectra as a tool for image presentation. In the proposed method similarity of selected spectral components in selected basic windows are used to similarity...
Continued advances of wireless communication technologies have enabled the deployment of large scale wireless sensor networks. The sensors' limited power makes energy consumption a critical issue. In single-hop wireless sensor networks, cluster heads election method based on residual energy can obtain better energy efficiency than the method in which cluster heads are elected in turns or by probabilities...
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