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Connected vehicles will likely use hybrid communication networks. Presumably a licence-free radio access technology (RAT) will be used for vehicle-to-vehicle (V2V) contact, complemented by a cellular network, with an associated usage cost. In previous work, we developed a self-adaptive clustering algorithm for reducing cellular access costs, while ensuring that clustering overheads do not saturate...
ROCK is a popular algorithm to cluster categorical data due to its ingenious concept of links between them. The only issue with this method is time complexity. The procedure is inherently slow with maximum iterations N-k. This paper shows how properties of dataset can be utilized to reduce the total iterations by a factor of 10 or more. The reduction is much significant as the size of dataset grows...
Clustering is useful for discovering underlying groups and identifying interesting patterns in scientific data and engineering systems. Affinity propagation (AP) is an effective clustering algorithm which has been successfully applied to broad areas of computer science. To generate high quality clusters, AP iteratively performs information propagation on the full similarity matrix and requires excessive...
In software projects, there is a data repository which contains the bug reports. These bugs are required to carefully analyse and resolve the problem. Handling these bugs humanly is extremely time consuming process, and it can result the deleying in addressing some important bugs resolutions. To overcome this problem, researchers have introduced many techniques. One of the commonly used algorithm...
Amount and diversity of data produced and processed has been dramatically increased parallel to improvements in technology. Unfortunately produced data usually don't have any labels which may make the classification and building information process more easily. This resulted with higher importance on data clustering for builing information. In this work K-Means, Spectral Clustering and Girvan-Newman...
With the rapid development of cellular network systems, the operators need more experience to deal with complicated network management system and wide range of Key Performance Indicators (KPIs). There are many indicators related to each other due to the definition or communication process. But several implicit associations still exist among these KPIs. This paper proposes an approach to figure out...
ST-DBSCAN is an extension of the traditional density based clustering algorithm DBSCAN. This algorithm is proposed for clustering spatio-temporal datasets. Spatio-temporal data refers to data which is stored as temporal slices of the spatial dataset. Here the spatial part of data identifies the location in data space and the temporal part of data represents the state in time. ST-DBSCAN can cluster...
Clustering of nodes in Wireless Sensor Networks is a problem of concern for many researchers. The major challenge is to propose an algorithm which can optimize the values of various performance parameters like packet delivery ratio and network lifetime of a node in the network. This paper shows an implementation of firefly algorithm to perform clustering in wireless sensor networks. Simulation results...
Code clones are code fragments identical to each other. Simple code clones are easily detected by automated clone detection tools. But huge numbers of simple clones are reported by these tools. Careful observation is required to extract useful information from this large number of simple clones. There are recurring patterns of simple clones which indicate design level similarities called structural...
Identifying regions of interest (ROIs) in images is a very active research problem as it highly depends on the types and characteristics of images. In this paper we present a comparative evaluation of unsupervised learning methods, in particular clustering, to identify ROIs in solar images from the Solar Dynamics Observatory (SDO) mission. With the purpose of finding regions within the solar images...
How can we retrieve meaningful information from a large and sparse graph?. Traditional approaches focus on generic clustering techniques and discovering dense cumulus in a network graph, however, they tend to omit interesting patterns such as the paradigmatic relations. In this paper, we propose a novel graph clustering technique modelling the relations of a node using the paradigmatic analysis. We...
Increasingly more popular cloud services have frequently many functional parts, which makes their structure rather complex yet its understanding improves network monitoring for security purposes, traffic routing, etc. Since the structure of third-party services is typically unknown, automated tools for its discovery are of great need. In this work, we propose such tool relying only on high-level statistics...
Digital video is becoming an emerging force in current computer and telecommunication industries for its large mass of data. Video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Key frame extraction is a very useful technique to provide a concise access to the video content and is the first step towards efficient browsing and retrieval...
With the aim of improving the immersive experience of the end user, High Dynamic Range (HDR) imaging has been gaining popularity. Therefore, proper validation and performance benchmarking of HDR processing algorithms is a key step towards standardization and commercial deployment. A crucial component of such validation studies is the selection of a challenging and balanced set of source (reference)...
Energy of sensor nodes is scarce resource in wireless sensor network. It is vital to reduce energy consumption to improve lifetime of wireless sensor network. A proficient way to improve lifetime is to partition sensor network into groups called cluster with high energy node acting as leader of the cluster called cluster head. Cluster head is responsible for managing intra-cluster and inter cluster...
The amount of resources needed to provision Virtual Machines (VM) in a cloud computing systems to support virtual HPC clusters can be predicted from the analysis of historic use data. In previous work, Hacker et al. found that cluster analysis is a useful tool to understand the underlying spatio-temporal dependencies present in system fault and use logs. However, the cluster analysis used for reducing...
This paper is concerned with the challenge of reorganising a software system into modules that both obey sound design principles and are sensible to domain experts. The problem has given rise to several unsupervised automated approaches that use techniques such as clustering and Formal Concept Analysis. Although results are often partially correct, they usually require refinement to enable the developer...
Aiming at the bad distribution uniformity of traditional clustering algorithm (LEACH algorithm) in wireless sensor networks, the cluster head node within the cluster is the provider of services, and the algorithm does not consider the process of service failure (link failure, and so on), according to the greatest expectations of coverage principle, we put forward the MEXCLP algorithm for clustering,...
The grey relational analysis is widely used in many fields, such as education, decision-making in economics, marketing research, medicine, computer science, system modeling, social science, chemistry, management, etc. In this paper, the algorithms between grey relational analysis and fuzzy c-mean are compared. Finally, one real data set was applied to prove that the performance of the Grey Relational...
Text document clustering plays an important role in the modern knowledge management. This paper addresses the task of developing an effective and efficient method of clustering the text document. To meet this requirement, we first extract the modifying relations (MR) from the sentences and then organize them as feature set for representing the document. A novel similarity measure is proposed on the...
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