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Partitioning of electric networks into zones or areas is a procedure that has numerous applications in power system planning, operation and control. Spectral clustering based approaches are among the most favoured ones to solve the partitioning problem. Applications of spectral clustering include definition of control zones, analysis of connectivity structure of power networks, intentional controlled...
We present a new algorithm for discovering clusters in noisy data streams using dynamic and cluster-specific temporal decay factors. Our improvement helps identify and adapt to evolving trends by adapting the weighting of stream data based on both content attributes and temporal arrival patterns. Our experimental results show that the proposed algorithm can discover better quality clusters in noisy...
A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation...
Due to their intrinsic characteristics, Wireless Sensor Networks (WSN) are prone to failure, mainly because of their energy limitations. Considering this, the use of self-healing mechanisms becomes necessary in order to create a more fault-tolerant and robust WSN. With this problem at hand, we have proposed and developed DACA, a Disjoint path And Clustering Algorithm that increases the network lifetime...
Automatic segmentation of fiber bundles can be beneficial to quantitative analysis on neuropsychiatric diseases. Previous clustering methods for fiber segmentation typically specify the number of clusters in advance or rely on prior knowledge. In this paper, we develop a new segmentation algorithm based on density-peaks clustering, in which the number of clusters can arise intuitively. This clustering...
Microbiome datasets are often comprised of different representations or views which provide complementary information, such as metabolic pathways, taxonomic assignments and gene families. Computational methods for integration of multi-view information combine these data to create a comprehensive view of a given microbiome study. Similarity network fusion (SNF) provides a candidate to solve this problem...
Non-negative matrix factorization (NMF) is an efficient dimension reduction method and plays an important role in many pattern recognition and computer vision tasks. However, conventional NMF methods are not robust since the objective functions are sensitive to outliers and do not consider the geometric structure in datasets. In this paper, we proposed a correntropy graph regularized NMF (CGNMF) to...
Real data often are comprised of multiple modalities or different views, which provide complementary and consensus information to each other. Exploring those information is important for the multi-view data clustering and classification. Multiview embedding is an effective method for multiple view data which uncovers the common latent structure shared by different views. Previous studies assumed that...
Nowadays, smart phones get increasingly popular which also attracted hackers. With the increasing capabilities of such phones, more and more malicious softwares targeting these devices have been developed. Malwares can seriously damage an infected device within seconds. In this paper, we propose to use the trimming approaches for automatic clustering (trimmed k-means, Tclust) of smartphone's applications...
Online Social Networks (OSNs) heavily rely on community detection algorithms to support many of their core services. Common functions such as friend recommendation, and timeline personalization all require the fast discovery of communities over some massive graph(s). For such applications, scalability, flexibility and speed are much more important than marginal improvement in the theoretical quality...
Nowadays, Smartphones have been widely used due to their capabilities in communication and multimedia processing. Smartphones provide access to a tremendous amount of sensitive information related to business, such as customer contacts, financial data, and Intranet networks. Hence, the Internet of the future will be mobile Internet. However, threat of malicious software has become an important factor...
The problem of hierarchical clustering items from pairwise similarities is found across various scientific disciplines, from biology to networking. Often, applications of clustering techniques are limited by the cost of obtaining similarities between pairs of items. While prior work has been developed to reconstruct clustering using a significantly reduced set of pairwise similarities via adaptivemeasurements,...
Defect-proneness prediction is affected by multiple aspects including sampling bias, non-metric factors, uncertainty of models etc. These aspects often contribute to prediction uncertainty and result in variance of prediction. This paper proposes two methods of data mining static code metrics to enhance defect-proneness prediction. Given little non-metric or qualitative information extracted from...
Intelligent Transportation Systems (ITS) aggregate, analyze and display geographic- and temporal-specific sensor information to reduce congestion while promoting safety. Until now, the coverage and potential of ITS have been restricted by the excessive cost of deploying the required road sensor and communications infrastructure. Our solution to this problem is focused on a novel integrated ITS Network...
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