Clustering

Clustering is to divide similar objects into different groups or more subsets by static classification. Members in the same subset have similar attributes. Cluster analysis can be regarded as an unsupervised learning technology.

Research and implementation of visual data clustering based on Hadoop platform University Lin Hadoop is a distributed model for solving large data storage and analysis problems. Clustering algorithm can make the feature expression of visual data through clustering generation code book. How to complete the clustering algorithm in the distributed model is an important problem in the research and production. Aiming at the problem of large-scale visual data clustering, this paper designs and realizes the visual data clustering algorithm based on Hadoop model, which improves the efficiency of visual data clustering. This paper first introduces the visual characteristics, analysis ...

In this recipe you set up a FortiGate Clustering Protocol (FGCP) virtual clustering configuration with two FortiGates to provide redundancy and failover protection for two networks.

In this recipe you set up a FortiGate Clustering Protocol (FGCP) virtual clustering configuration with four FortiGates to provide redundancy and failover protection for two networks.

The cloud economy is so compelling, today, more and more data center managers are starting to assess which additional applications are more valuable to cloud services, whether it is a private cloud that deploys its own enterprise, or whether it is a mix or a public cloud service that uses Amazon Network services or other service providers. At the same time, there is growing evidence that proven enterprise-class features are being looked at, as they provide reliable services to comply with SLA agreements. As data center managers become accustomed to using cloud services more and more, they don't want to give up ...

Research on distributed data stream clustering algorithm based on Hadoop mapreduce Cai Binlei Ningjiadong Zhu Shiwei Guo Qin with the continuous increase of data flow scale, the existing clustering algorithm based on grid has no effect on the clustering of data streams, can not find any shape clusters in real time, and can not delete the noise points in the data stream in time. This paper presents a distributed data stream clustering algorithm (Pgdc-stream) based on grid density in Hadoop platform environment, which facilitates the parallel clustering of data flow in MapReduce framework based on Hadoop.

Realization and optimization of k-medoids clustering algorithm based on Hadoop East China Normal University Shang based on the features of K-medoids algorithm and the advantages of Hadoop platform, the implementation of parallel K clustering algorithm implemented in Mahout open source project is referenced. In this paper, a parallel clustering algorithm based on MapReduce is proposed, which hk-medoids the operation rate of the traditional clustering algorithm greatly. In addition, in order to further improve the clustering efficiency, this article from the perfect mapreduce scheduling, take sampling method, pre ...

A method of clustering detection of malicious code model based on K-L divergence Bengenqing Liang Shao Bilin in cloud Computing application environment, because the service system is more and more complex, the network security loophole and the attack situation increase dramatically, the traditional malicious code detection technology and the protection pattern already cannot adapt the cloud storage environment the demand. Therefore, by introducing the Gaussian mixture model, establishing the layered detection mechanism of malicious code, analyzing and extracting the characteristic value of sample data by means of information gain and document frequency, and combining the K-L divergence characteristic, a malicious code model clustering detection method based on K-L divergence is proposed. Adopt KDDCU ...

A parallel clustering model based on MapReduce Gurechun in the process of clustering massive data, the limitation of traditional serial mode is more and more obvious, it is difficult to get satisfactory result in the effective time, this paper proposes a parallel clustering model based on the MapReduce framework under the Hadoop platform. The theoretical and experimental results show that the model has an acceleration ratio of near linear velocity and high efficiency for mass data. A parallel clustering model based on MapReduce

Intelligent management includes application versioning, dynamic clustering, health management, and intelligent routing. This article mainly introduces you to application version management. IBM released the WebSphere creator Server (WAS) V8.5 on June 15, 2012. A major change in was V8.5 is the complete incorporation of the functionality of the previously independent product WebSphere Virtual Enterprise (hereinafter referred to as WVE) into ...

The 3rd part of this XML data Mining series explains several concepts about clustered XML documents and describes the XML document cluster tasks to perform when the content and structure of the document change over time. In real-world applications, XML documents evolve from one version to another, and the number of changes to be implemented is unpredictable. It is normal for the original cluster solution to be eliminated after the change is implemented. To overcome this, this article describes a non-redundant methodology that can recalculate XML documents after a change ...

Research and realization of clustering and convex-package algorithm in MapReduce framework Chengdu University of Technology Zhaoju first, this paper makes a research on the generation and value growth of large data, and explains the necessity of improving the execution efficiency of the data mining algorithm, and introduces the technology and tools that support the large-data processing now. Then the paper studies the running mechanism of Hadoop file system, the stored procedure and the programming model of MapReduce framework, and the operation principle. Secondly, in a certain size of Hadoop cluster on the data distributed processing, so as to assess the whole cluster of sex ...

Micro-blog event detection and tracking based on representative point increment hierarchical density clustering in cloud computing environment Fung Han Nan Jadong to extract news events from a large number of real-time information generated from the microblogging service platform, a complete set of micro-blog event detection and tracking algorithm in cloud computing environment is proposed. Firstly, a new weight calculation method based on micro-Bo forwarding number and comment number is used to represent the micro-Bowenben as a vector space model, and then the incremental hierarchical density clustering (rihdbscan) algorithm based on the representative point is adopted to extract the keywords and finally realize the detection and tracking of news events. For single ...

K-tree is a tree-like clustering algorithm. It is also called tree structure vector quantization (TSVQ). The goal of clustering analysis is in similar group objects. Each K-tree object represents a dimension vector space. All vectors in the tree must be the same number of dimensions. K-tree provides a scalable clustering method combining B+-tree and K cluster algorithms. Clusters can be used to solve signal processing problems, using machine learning, and other environments. It has also recently been used to solve problems in the Wikipedia documentation cluster. K-tree 0 ...

December 17 Morning News, Microsoft yesterday for the poly-cool plagiarism apology, but has not made a more detailed description of the matter. At the same time, Plurk has not further disclosed details of plagiarism. If this event stays at the current stage, the source code leak path may be a mystery. Microsoft has been labeled with a copy. In the face of Plurk's accusations, Microsoft yesterday admitted that "part of the code is really a copy" of the Clustering services launched by MSN China. In fact, prior to this, Plurk has compared the interface between the two and part of the code screenshots of the public ...

Cloud computing Service selection based on clustering and Skyline computing Che Zenan Li Shiyang Computer Measurement and Control 2014 01 Cloud computing Services selection based on clustering and Skyline computing

Implementation of K Clustering in Cloud Computing Environnement the main aim of this work are to implement and deploy K a Lgorithm in Google Cloud using the Google App Engine with Cloud ...

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"Tenkine Server channel June 3 message" http://www.aliyun.com/zixun/aggregation/32730.html "> Information system is the place where the final results are saved and processed is the database. Therefore, the database system becomes particularly important, which means that if the database is facing problems, it means that the entire application system will also face challenges, resulting in serious losses and consequences. At present, in the large data trend, the database faces the following challenges: When the database performance encountered ...

Data mining, which mainly solves four kinds of problems, is a very clear definition of several kinds of problems that it can solve. This is a high degree of induction, and the application of data mining is a process to deduce these types of problems. So let's look at how the four types of problems it solves are defined: 1, classification problem classification problem is a predictive problem, but it is different from the general prediction problem, but the difference is that the results of its predictions are categories (such as a, B, C three) rather than a specific value (such as 55, 65, 75 ...). ...)。 ...