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检索条件"任意字段=2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016"

共 3352 条记录，以下是571-580 订阅

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Automatic mining of Multi-granularity Temporal Regularity from Trajectory data 17

Automatic Mining of Multi-granularity Temporal Regularity fr...

引用

10th international conference on Utility and Cloud computing (UCC) / 4th international conference on Big data computing, Applications and Technologies (BDCAT)

作者： Huang, Siyuan Zhang, Rui Li, Nuofei Guo, Jiming Jiang, Hongbo Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Comp Sci & Technol Hubei Key Lab Inland Shipping Technol Hubei Key Lab Transportat Internet Things Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

ISBN: (纸本)9781450355490

Temporal regularity in trajectory data is an important basis for traffic management, public service and marketing. Although many efforts have been made to study temporal regularity, yet almost all existing works select time granularity intuitively. User-specified time granularity and other parameters may lead to biased results. Moreover, as the size of datasets grows, the costs of parameters tuning also increases. To solve these problems, we propose the Automatic Multi-granularity Temporal Regularity Detection algorithm (auto-MTRD) for trajectory data. Our approach clusters time series from the trajectory data using automatic parameter selection and generates a temporal regularity tree to indicate multi-granularity temporal regularity. It cannot only avoid the negative effect of human intervention, but also evaluate the relative importance of multiple time granularities at the same time. Two real-life datasets are used to validate the effectiveness of our method.

关键词： trajectory data temporal regularity automatic mining

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Application of data mining techniques on olympic data

Application of data mining techniques on olympic data

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international conference on WWW/Internet 2017 and Applied computing 2017

作者： Farias, Eric Chagas Schünke, Marco Aurélio Da Silva, Paulo Rodrigo Pedroso Zeve, Carlos Mário Dalcol Curso de Ciencia da Computacao-Campus Guaíba Brazil Curso de Educacao Física - Campus Guaíba Brazil

ISBN: (纸本)9789898533692

This work seeks to explore the current scenario of news related to the Olympic Games of Rio de Janeiro 2016, using the process of knowledge discovery in databases. With the increase of the news divulged in the scope of the olympic sports, it is tried to relate the most diverse adjacent subjects like Olympic with its games in its totality and with this to identify patterns and relations for the vision of the producers of the content in the disclosure of news on games Olympic. The motivation of this analysis is to be able to understand what the current scenario of news related to the Olympics, how much access to this content in relation to the other inserted in the page, which is the content of these publications about each of the related Issues and who is the producer of this content, which feeling about the content and interest in the interrelation of these issues. © 2017.

关键词： data mining

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Fishing Monitor System data: A Naive Bayes Approach 16th

引用

16th international conference on Intelligent Systems Design and Applications (ISDA)

作者： Ferreira, Joao C. Lage, Serge Pinto, Iola Antunes, Nuno Inst Univ Lisboa ISCTE IUL Business Res Unit BRU IUL Lisbon Portugal ISEL Lisbon Portugal Univ Minho Ctr Algoritmi Guimaraes Portugal FCT UNL Ctr Matemat & Aplicacoes Lisbon Portugal Xsealance Sea Technol SA Leira Portugal

ISBN: (纸本)9783319534800;9783319534794

This paper discusses the results of an applied research on the fishing activity based on a monitor system developed by a company and fishing reports produced at the end of each fishing activity. Due to economic interests combined with fishing limitations there is a natural tendency for wrong reporting. We apply data mining (DM) methodologies to find fishing patterns. These DM techniques in SQL tool allow to deal with the high volume of this data set and determine the major factors that influence fishing activity.

关键词： Fishing activity data mining Naive Bayes Patterns

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mining Association Rules from a Dynamic Probabilistic Numerical dataset Using Estimated-Frequent Uncertain-Itemsets 1st

Mining Association Rules from a Dynamic Probabilistic Numeri...

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1st international conference on Smart computing and Communication (SmartCom)

作者： Pei, Bin Wang, Fenmei Wang, Xiuzhen New Star Res Inst Appl Technol Comp Res & Teaching Sect Hefei Anhui Peoples R China

ISBN: (纸本)9783319520155;9783319520148

In recent years, many new applications, such as location-based services, sensor monitoring systems, and data integration, have shown a growing amount of importance of uncertain data mining. In addition, due to instrument errors, imprecise of sensor monitoring systems, and so on, real-world data tend to be numerical data with inherent uncertainty. Thus, mining association rules from an uncertain, especially probabilistic numerical dataset has been studied recently. However, a probabilistic numerical dataset often grows as new data append. Thus, developing a mining algorithm that can incrementally maintain discovered information is quite important. In this paper, we have designed an efficient, incremental mining algorithm to mine association rules from a probabilistic numeric dataset using estimated-frequent uncertain-itemsets. By using a user-specified support threshold, estimated-frequent uncertain-itemsets could act as a gap to avoid small itemsets becoming large in the updated dataset when new transactions are inserted. As a result, the algorithm has execution time faster than that of previous methods. An illustrated example is given to demonstrate the procedures of the algorithm.

关键词： Association rule Dynamic probabilistic numerical dataset Estimated-frequent uncertain-itemsets data mining Uncertain data

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Securing patient’s confidential data in ECG steganography using advanced encryption standards

Securing patient’s confidential data in ECG steganography u...

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international conference on Communication and computing Systems, ICCCS 2016

作者： Singh, Suchita Kumar, Vimlesh School of ICT Gautam Buddha University Greater NoidaUttar Pradesh India

ISBN: (纸本)9781138029521

In today’s world, the number of elderly cardiac patients is growing tremendously, which arises the need of Point-of-Care (PoC) systems. Therefore, the Body Sensor Networks (BSNs) collect ECG signal from remote patients at their homes only and transmit it along with other physiological readings such as glucose level, temperature, blood pressure, etc. and patient’s personal data. All this information is transmitted to hospital servers through internet. As per the HIPPA guidelines, the confidentiality and integrity of the patient’s data must be maintained when it is transmitted over internet. In this paper, a combination of cryptography and steganography method is applied to hide patient’s secret information. The proposed method uses advanced Encryption Standards (AES) which encrypts and safeguards the integrity of data for 20–30 years. Image Quality Assessment Techniques such as Peak Signal-to-Noise Ratio and Mean Square Error are used for distortion measurement. It is concluded that the suggested system provides eminent level of safety and privacy for the patient’s information and ECG data can be easily diagnosed from the watermarked image as well as from the image obtained after removing the data from the watermarked image. © 2017 Taylor & Francis Group, London.

关键词： Steganography

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advanced Control Strategies for Irrigation Systems 9

Advanced Control Strategies for Irrigation Systems

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9th IEEE international conference on Intelligent data Acquisition and advanced computing Systems - Technology and Applications (IDAACS)

作者： Marinescu, Tiberiu Arghira, Nicoleta Hossu, Daniela Fagarasan, Ioana Stamatescu, Iulia Stamatescu, Grigore Calofir, Vasile Iliescu, Sergiu Univ Politehn Bucuresti Dept Automat & Ind Informat Fac Automat Control & Comp Splaiul Independentei 313Sect 6 Bucharest 060042 Romania

ISBN: (纸本)9781538606971

Water management and irrigation scheduling have become the main subjects of different studies in the last decades, due to their high influence on crop performance indicators. This study presents the most important parameters that have to be monitored in an irrigation management system and the most important ones are synthesized: air moisture and temperature, soil air and moisture, evapotranspiration. Based on the monitoring of these parameters, different control strategies and methods can be applied for optimization and efficiency of irrigation systems. The synthesis in this paper starts with classical control systems and, also, advanced methods such as fuzzy concept, decision support systems and model predictive control. Considering the currently necessity of integration into the Cyber-Physical Systems (CPS) concept, the paper finally proposes an irrigation control system for vineyards. The SCADA architecture is suitable for the Romanian context, it allows flexibility and ease of use and it reduced both energy consumption and irrigation operation costs.

关键词： irrigation telemetry control precision agriculture

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MapReduce Based Multilevel Association Rule mining from Concept Hierarchical Sales data 1st

MapReduce Based Multilevel Association Rule Mining from Conc...

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1st international conference on Advances in computing and data Sciences (ICACDS)

作者： Prajapati, Dinesh J. Garg, Sanjay Nirma Univ Inst Technol Comp Sci & Engn Dept Ahmadabad Gujarat India

ISBN: (纸本)9789811054273;9789811054266

Multilevel association rule mining is one of the important techniques of data mining to analyze the sales data. Multilevel association rules provide detailed information as compare to single level association rules. Today's era of e-commerce and e-business, various online marketing sites and social networking sites are generating tremendous amount of data in the form of sales, tweets, text mails, web usages and many more. The data generated from these sources is really too large so that it becomes tedious task to process and analyze using traditional approaches. This paper overcomes the drawback of single node computing by distributing the task to cluster of nodes. The performance of this system is analyzed using reduced minimum support threshold at different levels of concept hierarchy and by varying the database size. In this experiment, the transactional dataset is generated from big sales dataset then the distributed multilevel frequent pattern mining algorithm (DMFPM) is implemented to generate level-crossing frequent itemset using hadoop mapreduce framework. The multilevel association rules are generated from frequent itemset. The hierarchical redundant rule affects the efficiency of the system, so hierarchical redundancy is removed from it. Finally, the time efficiency of proposed algorithms is compared with existing Multilevel Frequent Pattern mining Algorithm (MFPM).

关键词： Distributed frequent pattern mining algorithm Multilevel association rule Mapreduce Level crossing rules Redundant rules

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data mining Classification Models for Industrial Planning 1st

Data Mining Classification Models for Industrial Planning

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1st international conference on Advances in computing and data Sciences (ICACDS)

作者： Braganca, Ricardo Portela, Filipe Vale, A. Guimaraes, Tiago Santos, Manuel Univ Minho Algoritmi Res Ctr Guimaraes Portugal Value Added Partners Porto Portugal

ISBN: (纸本)9789811054273;9789811054266

The data mining models are an excellent tool to help companies that live from the sale of items they produce. With these models combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This project is based on the phases of the methodology CRISP-DM. Several methods were applied to this data namely, average, mean and standard deviation, quartiles and Sturges rule. Classification Techniques were used in order to understand which model has the best probability of hitting the correct result. After performing the tests, model M1 was the one with the best chance to accomplish a great level of classification having 99.52% of accuracy.

关键词： data mining Classification CRISP-DM DSR Lean WEKA

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Comparative Study of Classification Techniques for Weather data 1st

Comparative Study of Classification Techniques for Weather D...

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1st international conference on Advances in computing and data Sciences (ICACDS)

作者： Panjwani, Shweta Kumar, S. Naresh Ahuja, Laxmi Indian Agr Res Inst Ctr Environm Sci & Climate Resilient Agr New Delhi 110012 India Amity Univ Amity Inst Informat Technol Noida Uttar Pradesh India

ISBN: (纸本)9789811054273;9789811054266

data mining techniques are widely used to analyze the large amount of data. Classification is an important technique which classifies data of various real world applications. This paper aims to compare the performance of classification algorithms for weather data using Waikato Environment for Knowledge Analysis (WEKA). Performance analysis done using cross fold and training set method. The best algorithm found was J48 Decision Tree classifier with highest accuracy and minimum error as compared to others.

关键词： Weather data data mining Classification Performance evaluation J48 decision tree Multi-layer Perceptron Bayes network

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mining Perfectly Rare Itemsets on Big data: An Approach Based on Apriori-Inverse and MapReduce 16th

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16th international conference on Intelligent Systems Design and Applications (ISDA)

作者： Padillo, F. Luna, J. M. Ventura, S. Univ Cordoba Dept Comp Sci & Numer Anal Rabanales Campus E-14071 Cordoba Spain

ISBN: (纸本)9783319534800;9783319534794

Association rule mining is one of the most common data mining techniques used to identify and describe interesting relationships between patterns from large quantities of data. Whereas many researches have been focused on the extraction of these patterns which appear frequently to obtain general information, in some scenarios it could also be interesting to extract unexpected phenomena. Rare association rule mining is a recent field aiming to discover sporadic rules having a low frequency of appearance but high confidence of occurring together. This field is really useful over Big data where abnormal endeavor are more interesting than normal behavior. In this sense, our aim is to propose a new algorithm to obtain rare association rule on Big data using MapReduce by means of Spark and Hadoop. The experimental study includes more than 30 datasets revealing alluring results in efficiency when more than 60, 000 million of instances and file sizes of 500 GBytes are considered.

关键词： Spark Hadoop Big data Association rules Rare itemsets

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