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作者： Weirui Feng Jiangxi Modern Polytechnic College

With the rapid development of society and information technology,data information is expanding at an alarming rate,and mankind has entered the era of big *** data spawned new changes in government management and public *** introduction of large data to government management has become an effective way to realize the modernization of the national governance system and the ability of governance,as well as the inevitable requirement for the realization of the modernization of *** paper first introduces the basic technical foundation of AIS protocol,the characteristics and application value of cloud computing technology,and finally uses distributed computing storage technology.

关键词： Cloud computing Data mining AIS dbscan algorithm

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CoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision

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BMC BIOINFORMATICS 2017年第Sup16期18卷 571-571页

作者： Herath, Damayanthi Tang, Sen-Lin Tandon, Kshitij Ackland, David Halgamuge, Saman Kumara Univ Melbourne Dept Mech Engn Melbourne Vic 3010 Australia Univ Peradeniya Dept Comp Engn Peradeniya 20400 Sri Lanka Acad Sinica Biodivers Res Ctr Taipei 11529 Taiwan Natl Tsing Hua Univ Inst Bioinformat & Struct Biol Hsinchu 300 Taiwan Acad Sinica Taiwan Int Grad Program Inst Informat Sci Bioinformat Program Taipei 115 Taiwan Univ Melbourne Dept Biomed Engn Melbourne Vic 3010 Australia Australian Natl Univ Res Sch Engn Coll Engn & Comp Sci Canberra ACT 2601 Australia

Background: In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning;however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm dbscan is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains. Results: Binning methods based on both compositional features and coverages of contigs had higher performances than the method which is based only on compositional features of contigs. CoMet yielded higher or comparable precision in comparison to the existing binning methods on benchmark datasets of varying complexities. MyCC (coverage) had the highest ranking score in F1-score. However, the performances of CoMet were higher than MyCC (coverage) on the dataset containing multiple strains. Furthermore, CoMet recovered contigs of more species and was 18-39% higher in precision than the compared existing methods in discriminating species from the sample of multiple strains. CoMet resulted in

关键词： Metagenomics Binning Contig coverage Contig composition dbscan algorithm

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Analysis of Methicillin-resistant Staphylococcus Aureus Using Apriori, dbscan, and K-means algorithms 16

Analysis of Methicillin-resistant Staphylococcus Aureus Usin...

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3rd International Conference on Biomedical and Bioinformatics Engineering (ICBBE)

作者： Lee, Min Young Yoon, Taeseon Hankuk Acad Foreign Studies 50Oedaero 54beon Gil Yongin Gyeonggi Do South Korea

ISBN: (纸本)9781450348249

Known as the most virulent. super-bacteria,. methicillin-resistant staphylococcus aureus (MRSA) is a bacterium that has developed a multi-resistance to standard types of antibiotics, including the penicillins and cephalosporins. Such difficulty to treat with antibiotics exposes MRSA patients to a severe danger-even to death-especially those who possess weakened immune systems. In attempts to create effective antibiotics for MRSA, previous researches have focused on proteins in localized areas of the genome. This research, however, aims to suggest a new angle of MRSA by analyzing the entire genome through bioinformatics. The research utilizes three data mining techniques: Apriori algorithm, dbscan algorithm, and K-means algorithm. Apriori algorithm shows that Leucine is predominant in the genome, a characteristic similar to pathogens that cause malaria, dengue, and lung cancer. dbscan algorithm and K-means algorithm provide a surprising result: while MRSA genome may seem like a lump of leucine, it actually consists of two separate parts, with a ratio of 7 to 3. This result suggests that each region may have evolved differently from the original strain of MRSA, or, only one of the two regions may account for the multi-resistance of MRSA, while the other serving another purpose.

关键词： MRSA super-bacteria Apriori algorithm dbscan algorithm K-means algorithm leucine

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Application of Data Mining in Library-Based Personalized Learning

INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING

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INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING 2017年第12期12卷 127-133页

作者： Luo, Lin Chongqing Radio & TV Univ Chongqing Peoples R China

this paper expounds to mine up data with the dbscan algorithm in order to help teachers and students find which books they expect in the sea of library. In the first place, the model that dbscan algorithm applies in library data miner is proposed, followed by the dbscan algorithm improved on demands. In the end, an experiment is cited herein to validate this algorithm. The results show that the book price and the inventory level in the library produce a less impact on the resultant aggregation than the classification of books and the frequency of book borrowings. Library procurers should therefore purchase and subscribe data based on the results from cluster analysis thereby to improve hierarchies and structure distribution of library resources, forging on the library resources to be more scientific and reasonable, while it is also conducive to arousing readers' borrowing interest.

关键词： Digital Library Clustering algorithm dbscan algorithm

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An efficient privacy protection in mobility social network services with novel clustering-based anonymization

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016年第1期2016卷 1页

作者： Chen, Zhi-Guo Kang, Ho-Seok Yin, Shang-Nan Kim, Sung-Ryul Konkuk Univ Dept Internet & Multimedia Engn Seoul South Korea

A popular means of social communication for online users has become a trend with rapid growth of social networks in the last few years. Facebook, Myspace, Twitter, LinkedIn, etc. have created huge amounts of data about interactions of social networks. Meanwhile, the trend is also true for offline scenarios with rapid growth of mobile devices such as smart phones, tablets, and laptops used for social interactions. These mobile devices enlarge the traditional social network services platform and lead to a greater amount of mobile social network data. These data contain more private information of individuals such as location, habit, and health condition. However, there are many analytical, sociological, and economic questions that can be answered using these data, so the mobility data managers are expected to share the data with researchers, governments, and/or companies. Therefore, mobile social network data is badly in need of anonymization before it is shared or analyzed widely. k-anonymization is a well-known clustering-based anonymization approach. However, the implementation of this basic approach has been a challenge since many of the mobile social network data involve categorical data values. In this paper, we propose an approach for categorical data clustering using rough entropy method with dbscan clustering algorithm to improve the performance of k-anonymization approach. It has the ability to deal with uncertainty in the clustering process and can effectively find arbitrarily shaped clusters. We will report the proposed approach and discuss the credibility by theoretical studies and examples. And experimental results on two benchmark data sets obtained from UCI Machine Learning Repository show that our approach is second to none among the Fuzzy Centroids, MMeR, SDR and ITDR, etc. with respect to the local and global purity of clusters. Since the clustering algorithm is a key point of k-anonymization for clustering mobile social network data, our experiment

关键词： dbscan algorithm Rough entropy Cluster purity Categorical data clustering k-anonymization Mobile Social network

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Colour FAST (CFAST) match: fast affine template matching for colour images

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ELECTRONICS LETTERS 2016年第14期52卷 1220-+页

作者： Jia, Di Cao, Jun Song, Wei-dong Tang, Xiao-liang Zhu, Hong Liaoning Tech Univ Sch Elect & Informat Engn Liaoning 125105 Peoples R China Liaoning Tech Univ Sch Geomat Liaoning 125105 Peoples R China Liaoning Tech Univ Sch Software Liaoning 125105 Peoples R China Intel Corp 4500 S Dobson Rd Chandler AZ 85224 USA

Fast-match is a fast and effective algorithm for template matching. However, when matching colour images, the images are converted into greyscale images. The colour information is lost in this process, resulting in errors in areas with distinctive colours but similar greyscale values An improved fast-match algorithm that utilises all three RGB channels to construct colour sum-of-absolute-differences (CSAD) is proposed, thus improving the sum-of-absolute-differences distance used in fast-match. In this algorithm, each pixel in the image is categorised by clustering them using density-based spatial clustering of applications with noise (dbscan) algorithm over the RGB vector, then the number of pixels in each category and the cumulative RGB values for each RGB channel are calculated to identify the centroid of each category. The RGB vector centroid is used as the CSAD decision criteria, and inverse of number of pixels in each category is used as the differentiating coefficient to construct a new similarity measure. Experiment results demonstrate that this algorithm has significant higher accuracy for matching colour images than the original fast-match algorithm.

关键词： image colour analysis image matching image resolution pattern clustering vectors CFAST-match CSAD decision criteria dbscan algorithm RGB channels RGB vector centroid colour image matching colour sum-of-absolute-differences cumulative RGB values density-based spatial clustering-of-applications-with-noise fast affine template matching similarity measure

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Anomaly Detection Based on Enhanced dbscan algorithm

Anomaly Detection Based on Enhanced DBScan Algorithm

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Chen, Zhenguo Li, YongFei N China Inst Sci & Technol Dept Comp Beijing 101601 Peoples R China

As the Internet becomes more pervasive, it is very important that the security mechanisms of a system are designed so as to prevent unauthorized access to system resources and data. The paper proposes a clustering algorithm that exploits enhanced dbscan algorithm in anomaly detection. The algorithm that can be used for mass data processing turns into the hot research point of anomaly detection, to form normal behavior profile on the audit records and adjust the profile timely as the program behavior changed. The experimental result shows that the anomaly detecting based on enhanced dbscan algorithm can a higher detection rate and a low rate of false positives of DARPA data sets. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： dbscan algorithm Anomaly Detecting Intrusion Detection

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The Method of Detecting AIS Isolated Information Based on Clustering and Distance 2

The Method of Detecting AIS Isolated Information Based on Cl...

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2nd IEEE International Conference on Computer and Communications (ICCC)

作者： Li, Yan Zhang, Yingjun Zhu, Feixiang Dalian Maritime Univ Coll Nav Dalian Peoples R China

ISBN: (纸本)9781467390262

For the growing number of Automatic Identification System (AIS) data, people have begun to apply data mining and machine learning techniques to extract valuable information from AIS data. About the AIS information, the analysis of isolated information is of great significance. They represent some position points apparently deviation from the traffic flow, which may represent the abnormal reception of AIS data or the abnormal behavior of the ships. In order to detect AIS isolated information, this paper uses the dbscan algorithm to cluster AIS data which can identify arbitrarily shapes firstly, the main traffic flow of data acquisition area is extracted;then provide a method to judge whether other clusters belonging to the isolated cluster, next propose an isolated point judgment method, apply the method for the noise points identified by the process of clustering, and get eventually isolated points. Finally, through the analysis of these isolated information by overlap chart and database query, the information has certain value to the analysis of the maritime traffic safety by Vessel Traffic Service (VTS) and the analysis of the abnormal behavior of ships.

关键词： data mining dbscan algorithm isolated information AIS data

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Anomaly Detection Based on Enhanced dbscan algorithm

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Procedia Engineering 2011年 15卷 178-182页

作者： Zhenguo Chen Yong Fei Li Department of Computer North China Institute of Science and Technology East Yanjiao Beijing 101601 China

关键词： dbscan algorithm Anomaly Detecting Intrusion Detection

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A "Density-Based" algorithm for Cluster Analysis Using Species Sampling Gaussian Mixture Models

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2014年第4期23卷 1126-1142页

作者： Argiento, Raffaele Cremaschi, Andrea Guglielmi, Alessandra CNR IMATI I-20133 Milan Italy Univ Kent Sch Math Stat & Actuarial Sci Canterbury Kent England Politecn Milan Dipartimento Matemat I-20133 Milan Italy

We propose a new model for cluster analysis in a Bayesian nonparametric framework. Our model combines two ingredients, species sampling mixture models of Gaussian distributions on one hand, and a deterministic clustering procedure (dbscan) on the other. Here, two observations from the underlying species sampling mixture model share the same cluster if the distance between the densities corresponding to their latent parameters is smaller than a threshold;this yields a random partition which is coarser than the one induced by the species sampling mixture. Since this procedure depends on the value of the threshold, we suggest a strategy to fix it. In addition, we discuss implementation and applications of the model;comparison with more standard clustering algorithms will be given as well. Supplementary materials for the article are available online.

关键词： dbscan algorithm Bayesian nonparametrics Dirichlet process

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