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This paper summarizes some hot communication topics from January to February in 2013 by combining academic views of some scholars. These topics include the reasons for the failure of The Daily, big data and data mining, the transformation of newspapers and the two media markets in mainland China. The development of new media and the transformation of traditional media are always the topics that can draw lots of attention. The competition and cooperation between them can not only show the development of new techniques, but also play an important role in charging the habits of audiences, the mode of reporting news and some communication theories. More and more participation of pubic will bring much more challenges for the model of the guidance of public opinion in China. In order to adapt to the new broadcasting environment, the media needs more attempt and innovation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement of medical technology and big data era, how to protect the privacy of sensitive information in patients ' medical records has become a current research focus issue. In order to protect the privacy of medical big data in the publishing process, this paper proposed an AUR-Tree differential privacy data publishing algorithm. The algorithm used the attribute utility value ranking method to measure the influence degree of the quasi-identity attributes on the sensitive attributes, which was as the measurement basis for iterative segmentation. Then it ad opted top-down iterative segmentation classification tree technology based on generalization, and reasonably allocated the privacy budget through the class arithmetic method to realize the privacy protection in the medical data release process. The experimental results show that the algorithm retains the value of subsequent data mining and greatly improves the security, effectiveness and usability of the data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Big data processing is a technical field that has received wide attention and research in recent years. Data mining，as a technology for mining hidden valuable information from a large amount of data，is an effective tool for processing big data. This paper mainly classifies and summarizes the research status of big data processing algorithms from the perspective of data mining. Firstly，the methods of big data classification for streaming data are introduced，including single-model algorithms and integrated classification algorithms. Secondly，the clustering methods and association rule mining methods for big data are summarized respectively from the perspective of single-machine algorithms and multi-machine algorithms based on distributed parallel platforms. Finally，the existing research progress of the big data-oriented data mining algorithm is summarized and the prospect of future development trend is put forward. [ABSTRACT FROM AUTHOR]

The shuffling algorithm based on folding technique has ideal data scrambling effect and can meet the prerequisite of big data sampling. In order to prove that the internal rules of the scrambled data set are not destroyed after sampling, this paper analyzed the association rules of the data before and after sampling by data mining, and compared the support and confidence of the association rules as well as the frequency of the transactions, and found that the association rules of the data after the collapsed shuffle algorithm were stable before and after sampling. And by comparing with the time efficiency of the existing algorithm and the overall sampling error, it is further concluded that big data sampling is effective. Which means that the overall situation of the data can be inferred from the sampled sample. [ABSTRACT FROM AUTHOR]

Copyright of China Media Report Overseas is the property of Edmondson Intercultural Enterprises and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Current clustering methods study data clustering problems only from one angle, it is insufficient to considerate clustering chaotic big data of Internet of Things based on cloud pattern with low clustering quality. To achieve agile, intelligent and stable clustering on big data of Internet of Things, with studying general cloud pattern description models on events of Internet of Things, general cloud pattern analysis models on chaotic correlation features of events of Internet of Things, extracting algorithms on chaotic correlation features of events of Internet of Things based on cloud pattern, correlation mining of big data of Internet of Things based on cloud pattern chaotic correlation features, improved decompositing singular value algorithms, grid coupling clustering algorithms, K-means algorithms, decision tree learning methods, methods of analysis principal components, stratification merging methods and distribution probability function, this paper designed an agile, intelligent and stable clustering algorithm on big data of Internet of Things based on chaotic correlation features of events. Finally, it carried out validating experiments, and compared performance of this proposed algorithm with traditional algorithms. Experimental results show this algorithm has shorter clustering time, less error and higher agility, has better intelligence, dynamic evolution, stability than those of traditional algorithms. Therefore, this proposed algorithm achieves effective clustering on big data of events of Internet of Things with chaotic correlation features based on cloud patterns, has higher utility. [ABSTRACT FROM AUTHOR]

Aiming at the problem of extensibility of big data analysis on massively parallel distributed systems and software platforms, this paper proposed a big data mining technique based on parameter-free CLUBS algorithm around centroids. The technique worked in a completely unsupervised manner, splitting clusters based on minimum quadratic distance criteria to separate data from noise, identified blocks containing only outliers by intermediate refinement and generated complete clusters for the remaining blocks, it designed a parallelized version of CLUBS to enable fast and efficient clustering of big data. Experiments show that the CLUBS parallel algorithm is not affected by data dimension and noise, and is better than the existing algorithms in terms of scalability and execution time. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Because any MapReduce job requires a series of complex operations such as task scheduling and resource allocation independently, there are a lot of redundant disk I/O and resource duplicate application operations among multiple MapReduce jobs coordinated by the same algorithm, causing inefficient resource utilization in job computing process. Big data mining algorithms are usually divided into several MapReduce Jobs, taking ItemBased algorithm as an example, this paper analyzed the resource efficiency of mining algorithm with multi-MapReduce job collaboration scenario. It proposed an ItemBased algorithm based on Distributed Cache, which used Distributed Cache to cache I/O data between multiple MapReduce Jobs, broke the defect of independence between jobs, and reduced the waiting delay between Map and Reduce tasks. The experimental results show that, DistributedCache can improve the data reading speed of MapReduce jobs. The algorithm reconstructed by Distributed Cache greatly reduces the waiting delay between Map and Reduce tasks, and improves the resource efficiency by more than three times. [ABSTRACT FROM AUTHOR]

Aiming at the limitation that the existing big data classification methods cannot meet the time and storage space in big data applications, this paper proposed a design method of big data parallel multi-label K-nearest neighbor classifier based on Apache Spark framework. In order to reduce the cost of the existing MapReduce scheme by using other memory operations, firstly, it divided the training set into several partitions in conjunction with the parallel mechanism of the Apache Spark framework. Then in the map stage, it found the K-nearest neighbors of each partition of the sample to be predicted, and in the reduce phase, it determined the final K-nearest neighbors according to the results of the map phase. Finally, it aggregated the neighboring tag sets in parallel, and output the target tag set of the sample to be predicted by maximizing the posterior probability. Experiments were carried out on four big data classification datasets such as Poker Hand. The proposed method achieves a lower Hamming loss and proves its effectiveness. [ABSTRACT FROM AUTHOR]

Apriori is one of the most widely used algorithm to discover frequent patterns. However,scanning the entire dataset in each iteration makes this algorithm inefficient and hard to he in parallel. With the size of datasets gets larger continuously, this problem is becoming more and more serious. Therefore, this paper proposed a novel algorithm called IEBDA. This algorithm was a kind of parallelization of Apriori based on item encoding and Spark framework. Saving information of each itemset by item encoding so that it could finish frequent itemset mining without scanning the whole dataset repeatedly. The broadcast variables of Spark enabled this algorithm to be in parallel. Compared with other distributed Apriori algorithms on datasets with different sizes, the acceleration of mining after the first iteration was obvious. The results show that this algorithm efficiently improves the multi-iteratively frequent itemset mining in big data environment. [ABSTRACT FROM AUTHOR]

Association rules mining is an important research topic in data mining. Big data processing requests higher requirements for the efficiency of association rules mining algorithm, where the most time consuming step is frequent pattern mining. For the problem that the state of art frequent pattern mining algorithm was not efficient, this paper proposed a frequent pattern mining algorithm based on interval interaction and transaction mapping (IITM), which combined Apriori algorithm and FP-growth algorithm. This algorithm just needed to scan the dataset twice to generate the FP tree, and then scaned the FP tree to map the ID of each transaction to the interval. It grew the frequent pattern by interval interaction and solved the problems including the Apriori algorithm needed to scan the dataset multiple times, and the FP-growth algorithm needed to iterate to generate the conditional FP tree, which reduced the efficiency of the frequent pattern mining. Experiments on real dataset show that the IITM algorithm is superior to Apriori, FP-growth, and PIETM algorithms at different support. [ABSTRACT FROM AUTHOR]

The public health research faces new challenges and opportunities in the big data era. To promote the application of big data in public health, to understand its basics connotation precisely and to achieve the goal of improving people's health problem by developing solutions to mine information from the big data, this paper presents the current situation of big data in public health field. It shows that big data will form by collecting and tidying public health related data from different sources. Through in-depth mining and analysis, information relating to disease spread and health threaten would be uncovered and evaluated. Accordingly, measures and suggestions could be made to prevent threatens, to protect peoples health, and to reduce total medical costs. It is also found that by combining with bioinformatics technology, public health research techniques such as data acquisition, system management, information security, and application would have a large room to grow. In conclusion, the application of computer technology, the development of big data mining method, and the cultivation of public health related personnel, are effective factors in the development of this field. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SVM algorithm and naive Bayesian classification algorithm are the good performance of classification algorithm for complex data classification. However, they also have significant drawbacks so their classification are influenced and the traditional data mining classification algorithm can not meet the need of mass data processing. To solve these problems, this paper analyzed traditional naive Bayesian classification algorithm and raised improvement suggestions for it, brought forward the SVM_WNB classification algorithm. Ten it conducted a parallelization processing on Hadoop cloud platform so that it could process mass data. Finally, through experimental verification, the new algorithm has obvious improvement in terms of its accuracy and efficiency. It can be concluded that the algorithm can be applied to large data classification, and will play a significant effect. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Concerning the dissimilarity between original spatial data and progressively transmitted spatial data, this paper proposes a model for measuring the similarity of curve datasets. After curve similarity is measured based on differences of the topological and geometric characteristics, the weighted similarity and the similarity precision for curve datasets are computed. The model uses weighted similarity to assess the overall degree of similarity for curve datasets and uses similarity precision to assess the similarity differences between curves. Finally, the model was used in an experiment and verified for effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Journal of East Asian Studies is the property of National Taiwan Normal University, College of International Studies & Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)