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Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best known algorithm for k-means with a provable guarantee is a simple local search heuristic yielding an approximation guarantee of 9+≥ilon, a ratio that is known to be tight with respect to such methods.We overcome this barrier...
Identifying families of malware is today considered a fundamental problem in the context of computer security. The correct mapping of a malicious sample to a known family simplifies its analysis and allows experts to focus their efforts only on those samples presenting unknown characteristics or behaviours, thus improving the efficiency of the malware analysis process. Grouping malware in families...
The article focuses on the results of the research into scientific publications of the All-Russian Institute for Scientific and Technical Information of the Russian Academy of Sciences database (VINITI Database RAS) in different fields. The purpose of operation was to increase partition accuracy on the directions of large volumes of scientific data. This analysis was carried out on summaries of scientific...
Text mining discover and extract useful information from documents, whenever increase the size and number documents leads to redouble features. The huge features for the documents adds challenge to text mining called high dimension. The aim of this proposed study is minimize the high dimension of the documents, and improve Arabic text mining using clustering. In order to achieve this goal, we propose...
This paper presents a new approach of clusteringmechanism in the wireless sensor networks. In our proposedapproach the selection of the cluster head is based on multiplecriteria by combining varieties of performance metrics. Therefore, the main aim of this paper is to enhance the election of themost performed node in order to be a cluster head. The proposedmechanism is evaluated by Matlab and was...
Several research tools and projects require groups of similar code changes asinput. Examples are recommendation and bug finding tools that can providevaluable information to developers based on such data. With the help ofsimilar code changes they can simplify the application of bug fixes and codechanges to multiple locations in a project. But despite their benefit, thepractical value of existing tools...
Improvements to sensor devices including micro-electro mechanical devices that are used for information collection and dissemination has led to the introduction of Wireless Sensor Networks (WSN). Sensor nodes in a WSN are deployed over an area to collect data from the surroundings and to perform additional actions including data aggregation and storage, computations and data transmission to gateway...
Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the clustering of point patterns - sets or multi-sets of unordered elements - that are found in numerous applications and data sources. In this paper, we...
Many clustering evaluation methods are computed as a ratio between two objectives, typically these objectives express the compactness of all clusters while trying to maximize the separation between individual clusters. However, the clustering process itself is typically implemented as a single objective problem: optimizing a linear combination of between-points closeness. We propose MoCham - a hierarchical...
This paper discusses the Correntropy Induced Metric (CIM) based Growing Neural Gas (GNG) architecture. CIM is a kernel method based similarity measurement from the information theoretic learning perspective, which quantifies the similarity between probability distributions of input and reference vectors. We apply CIM to find a maximum error region and node insert criterion, instead of euclidean distance...
Black skin lesion images generally show low contrast between lesion parts and safe ones. Segmentation of such images is a challenging task. In this paper, we propose a novel method that first select safe likely skin region using a Multi-Layer Perceptron (MLP) Neural Network. Then, color of these selected regions is transformed, in order to increase the contrast with lesion regions. The obtained image...
This paper proposes a new semi-supervised clustering framework to represent and integrate quantitative preferences on attributes. A new metric learning algorithm is derived that achieves a compromise clustering between a data-driven and a user-driven solution and converges with a good complexity. We observe experimentally that the addition of preferences may be essential to achieve a better clustering...
Healthcare spending has been increasing in the last few decades. One of the main reasons for this increase is hospital readmissions, which is defined as a re-hospitalization of a patient after being discharged from a hospital within a short period of time. The excessive amount of money spent every year on hospital readmissions and the urge to enhance healthcare quality make reducing hospital readmissions...
This paper presents a distributed clustering algorithm, called DCEV, which constructs multi-hop clusters. DCEV places vehicles into non-overlapping clusters which have adaptive size based on their relative mobility. The cluster formation is based on a D-hop clustering scheme where each node selects its cluster head in at most D-hop distance. To create clusters, DCEV uses a new metric to let vehicles...
In this paper, a configurable many-core hardware/software architecture is proposed to efficiently execute the widely known and commonly used K-means clustering algorithm. A prototype was designed and implemented on a Xilinx Zynq-7000 All Programmable SoC. A single core configured with the slowest configuration achieves a 10× speed-up compared to the software only solution. The system is fully scalable...
Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements,...
Load Patterns (LPs) clustering has a broad range of applications, such as tariff formulation, power system planning, load forecasting, and so on. In this paper, we develop a multi-objective version of Differential Evolution (DE) using a Pareto Tournament (PT) selection to solve the LP clustering problem. Our automatic DE LP clustering (ADE-LPC) algorithm provides an entire Pareto front, and by incorporating...
A Mobile Ad hoc Network (MANET) is a multi-hop wireless network in which the mobile nodes are dynamic in nature and has a limited bandwidth and minimum battery power. Due to this challenging environment the mobile nodes can be grouped into clusters to achieve better stability and scalability. Grouping the mobile nodes is called clustering, in which a leader node is elected to manage the entire network...
Telecom Networks produce huge amount of daily alarm logs. These alarms usually arrive from different regions and network equipments of mobile operators at different times. In a typical network operator, Network Operations Centers (NOCs) constantly monitor those alarms in a central location and try to fix issues raised by intelligent warning systems by performing a trouble ticketing based management...
Breast cancer is a highly heterogeneous disease and very common among women worldwide. Inter-observer and intra-observer errors occur frequently in analyzing the lesion portion of medical images, giving high variability in results interpretations. Computer Aided Diagnosis system (CAD) plays a vital role to overcome this variability. Segmentation is the second critical stage in CAD system to extract...
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