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Data Mining: The 2016 International Conference Proceedings

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WorldComp is an international conference that serves researchers, scholars, professionals, students, and academicians who are looking to both foster working relationships and gain access to the latest research results. It is being held jointly (same location and dates) with a number of other research conferences; namely, The 2016 World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP'16). The Congress is among the top five largest annual gathering of researchers in computer science, computer engineering and applied computing. We anticipate to have attendees from about 85 countries/territories.

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SESSION Real-World Data Mining Applications, Challenges, and Perspectives

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Int'l Conf. Data Mining | DMIN'16 |

1

SESSION

REAL-WORLD DATA MINING APPLICATIONS,

CHALLENGES, AND PERSPECTIVES

Chair(s)

Mahmoud Abou-Nasr

Robert Stahlbock

Gary M. Weiss

ISBN: 1-60132-431-6, CSREA Press ©

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Int'l Conf. Data Mining | DMIN'16 |

ISBN: 1-60132-431-6, CSREA Press ©

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Clustering and Prediction of Solar Radiation Daily Patterns

1 Dipartimento

G. Nunnari1 , and S. Nunnari1 di Ingegneria Elettrica, Elettronica e Informatica, Universitá di Catania, Catania, Italy

Abstract— This paper addresses the problem of clustering daily patterns of global horizontal solar radiation by using a feature-based approach. A pair of features, referred to as

Sr and Hr , representing a measure of the normalized daily solar energy and of the energy fluctuations, respectively, is introduced. Clustering allows to perform some useful statistics at daily scale such as estimating the class weight and persistence. Furthermore, the problem of one-day ahead prediction of the class is addressed by using both hidden Markov models (HMM) and Non-linear Autoregressive

 

SESSION Data Science and Data Services

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Int'l Conf. Data Mining | DMIN'16 |

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SESSION

DATA SCIENCE AND DATA SERVICES

Chair(s)

Mahmoud Abou-Nasr

Peter Geczy

Robert Stahlbock

Gary M. Weiss

ISBN: 1-60132-431-6, CSREA Press ©

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DL4MD: A Deep Learning Framework for Intelligent Malware

Detection

William Hardy, Lingwei Chen, Shifu Hou, Yanfang Ye∗ , and Xin Li

Department of Computer Science and Electrical Engineering

West Virginia University, Morgantown, WV 26506, USA

Abstract- In the Internet-age, malware poses a serious and evolving threat to security, making the detection of malware of utmost concern. Many research efforts have been conducted on intelligent malware detection by applying data mining and machine learning techniques. Though great results have been obtained with these methods, most of them are built on shallow learning architectures, which are still somewhat unsatisfying for malware detection problems. In this paper, based on the Windows Application Programming

 

SESSION Segmentation, Clustering, Association + Web/Text/Multimedia Miningn+ Software

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Int'l Conf. Data Mining | DMIN'16 |

131

SESSION

SEGMENTATION, CLUSTERING, ASSOCIATION +

WEB / TEXT / MULTIMEDIA MINING +

SOFTWARE

Chair(s)

Diego Galar

Peter Geczy

Robert Stahlbock

Gary M. Weiss

ISBN: 1-60132-431-6, CSREA Press ©

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ISBN: 1-60132-431-6, CSREA Press ©

Int'l Conf. Data Mining | DMIN'16 |

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String Vector based AHC as Approach to Word Clustering

Taeho Jo

Department of Computer and Information Communication Engineering, Hongik University, Sejong, South Korea

Abstract— In this research, we propose the string vector based AHC (Agglomerative Hierarchical Clustering) algorithm as the approach to the word clustering. In the previous works on text clustering, it was successful to encode texts into string vectors by improving the performance of text clustering; it provided the motivation of doing this research.

In this research, we encode words into string vectors, define the semantic operation on string vectors, and modify the

 

SESSION Regression and Classification

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Int'l Conf. Data Mining | DMIN'16 |

185

SESSION

REGRESSION AND CLASSIFICATION

Chair(s)

Diego Galar

Robert Stahlbock

Gary M. Weiss

ISBN: 1-60132-431-6, CSREA Press ©

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ISBN: 1-60132-431-6, CSREA Press ©

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A Cross-Validation Method for Linear Regression Model Selection

Jingwei Xiong Junfeng Shang

Department of Mathematics and Statistics

Bowling Green State University

Bowling Green, OH 43403, USA

Abstract: In linear regression model setting, motivated by Wasserman and Roeder (2009), we develop a cross-validation procedure for selecting an appropriate model which can best fit the data. In the procedure, we make use of adaptive Lasso method to select the most appropriate model. In the selection of the suitable tuning parameter, the Bayesian Information Criterion (BIC, Schwarz, 1978) is utilized. We conduct the hypothesis testings for the significance of nonzero coefficients of fixed effects to further select the model. A simulation study investigates the effectiveness of performance for the proposed procedure. The simulation results demonstrate that BIC and the adaptive Lasso method can both lower Type

 

Late Posters

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Int'l Conf. Data Mining | DMIN'16 |

243

SESSION

LATE POSTERS

Chair(s)

TBA

ISBN: 1-60132-431-6, CSREA Press ©

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ISBN: 1-60132-431-6, CSREA Press ©

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245

Real-Time Classification of User Clicked News for Category

Proportion Adjustable News Recommendation

Zhegao Piao, Seong Joon Yoo and Yeong Hyeon Gu

Abstract—There have been studies on personalized news recommendation using collaborative filtering mechanism based on users' click behaviors. However, few existing studies have focused recommending news depending on the rates of the news categories interests. In this paper, we present a personalized news recommendation system which builds profiles of users' news categories interests, determines the number of news articles to be recommended for each news category in proportion to news categories interests and ranks news articles according to the user's news interests. In order to find the news categories that are interesting to read, the smart device collects the web pages viewed by the user and classifies the contents. We use machine learning techniques in order to classify web pages into different categories and experimental results show that Naïve

 

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