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A new closed frequent itemset mining algorithm based on GPU and improved vertical structure

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第6期29卷 1-12页

作者： Li, Yun Xu, Jie Yuan, Yun-Hao Chen, Ling Yangzhou Univ Coll Informat Engn Yangzhou 225009 Jiangsu Peoples R China

Vertical data structure is very important for closed frequent itemset mining. All closed frequent itemsets-can be found by simply using the operations of AND/OR. However, it consumes a large amount of storage space, especially in the case of large-size dataset. This paper proposes an algorithm for mining closed frequent itemsets based on a new vertical data structure. The proposed data structure is helpful to save storage space by using a multi-layer index. At the same time, numerous CPU and graphics processing unit can be employed in parallel to achieve high-efficiency computing. Especially when dealing with large datasets, the proposed algorithm can obtain a high-speed computing with the help of graphics processing unit. The improved vertical structure reduces the storage space of the data. The experimental results show that our proposed algorithm requires much less computation time than other related methods. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： data mining closed frequent itemset vertical data structure GPU

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Land Use Change Automatic Monitoring Using FPCR-ISOdata Method for a Rapid Urbanizing Region: Qingpu District in Shanghai City, China

Land Use Change Automatic Monitoring Using FPCR-ISODATA Meth...

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2017 3rd international conference on Artificial Intelligence and Industrial Engineering (AIIE2017)

作者： Xuan LI John MELIGRANA Cheng LI Feng XIE School of Rail Transportation Soochow University School of Urban and Regional Planning Queen's University Institute of Modern Optical Technologies Soochow University

With the rapid development of China's urbanization, land use automatic monitoring is imperative for the efficient allocation of resources and environmental conservation, especially in a metropolitan area with increased ecological-environmental stress as a result of large high-density population. Qingpu, a district of Shanghai, is located in Upper Huangpu Catchment for fresh water supply and an area of considerable ecological value, but it has been also experiencing development pressures from urban sprawl. An automatic procedure was proposed by using remotely sensed mass data to reveal the land use change in Qingpu District. In this procedure, historical remotely sensed data were first registered into the same spatial resolution and radioactive level. Then, data were subset via the boundary of study area for facilitating the following processing. Forward principal components rotation(FPCR) and iterative self-organizing data analysis techniques algorithm(FPCR-ISOdata) was developed to decorrelate or denoise data and divided the data into k groups(k>the number of land use types). After merging the same connoted groups and correcting misclassified units with promising accuracy, the series of land use map are created. The results further elaborated on the spatial-temporal change of agricultural land in Qingpu along with other land use changes over the past two decades. The conversions between land uses were shown as bare land appearing in a state of uncompleted development or transition. The results were consistent with reality and the method was accurate and practical.

关键词： Remote sensing Land use change Automatic monitoring Classification Multi-temporal

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data mining in healthcare informatics: Techniques and applications

Data mining in healthcare informatics: Techniques and applic...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Tanvi Anand Rekha Pal Sanjay Kumar Dubey Computer Science and Engineering Department Amity University Uttar Pradesh Noida U.P. India

ISBN: (纸本)9781467394178

The evolution of modern approach in knowledge systems, decision support systems and clinical constraints estimation algorithms that formulate machine learning, soft computing and data mining in presenting a new outlook for health informatics domain. Health is then clearly understood as the essential part while describing a person's sense of well-being. The delivery of health care services therefore considers as higher proportion, and played an efficient role in information and communication technologies for its effective distribution mechanism. data mining in health informatics are developing into optimistic area for producing vision from diverse data set. data mining techniques are proved to be as a valuable resource for health care informatics. The main scope of writing this paper is to analyse the effectiveness of data mining techniques in health informatics and compare various techniques, approaches or methods and different tools used and its effect on the healthcare industry. The main motive of using data mining application in healthcare systems is to exploit a machine driven tool for identifying and circulating useful and relevant healthcare information.

关键词： Decision support systems Handheld computers conferences

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Preface

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Advances in Intelligent Systems and computing 2017年 556卷 v-vi页

作者： Behera, Himansu Sekhar Mohapatra, Durga Prasad Department of Computer Science and Engineering & Information Technology Veer Surendra Sai University of Technology SambalpurOdisha India RourkelaOdisha India

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Classification Model for Forest Fire Hotspot Occurrences Prediction Using ANFIS Algorithm

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IOP conference Series: Earth and Environmental science 2017年第1期54卷

作者： A K Wijayanto O Sani N D Kartika Y Herdiyeni Center for Environmental Research Bogor Agricultural University Indonesia Gedung PPLH lt 3 Jl Lingkar Akademik Kampus IPB Darmaga Bogor Indonesia 16680 Master of Science in Information Technology for Natural Resources Management Bogor Agricultural University SEAMEO BIOTROP Campus km 6 Tajur Bogor West Java Indonesia 16721 The Agency of The Assessment and Application of Technology (BPPT) Jakarta Indonesia Computational Intelligence Lab Department of Computer Science Bogor Agricultural University Darmaga Campus Wing 20 Level V-VI Bogor West Java Indonesia 16680

This study proposed the application of data mining technique namely Adaptive Neuro-Fuzzy inference system (ANFIS) on forest fires hotspot data to develop classification models for hotspots occurrence in Central Kalimantan. Hotspot is a point that is indicated as the location of fires. In this study, hotspot distribution is categorized as true alarm and false alarm. ANFIS is a soft computing method in which a given inputoutput data set is expressed in a fuzzy inference system (FIS). The FIS implements a nonlinear mapping from its input space to the output space. The method of this study classified hotspots as target objects by correlating spatial attributes data using three folds in ANFIS algorithm to obtain the best model. The best result obtained from the 3rd fold provided low error for training (error = 0.0093676) and also low error testing result (error = 0.0093676). Attribute of distance to road is the most determining factor that influences the probability of true and false alarm where the level of human activities in this attribute is higher. This classification model can be used to develop early warning system of forest fire.

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CSFW-SC: A high-dimensional clustering algorithm based on cuckoo search 3

CSFW-SC: A high-dimensional clustering algorithm based on cu...

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3rd international conference on Cyberspace Technology, CCT 2015

作者： Wang, Jindong He, Jiajing Zhang, Hengwei Yu, Zhiyong Zhengzhou Institute of Information Science and Technology Zhengzhou 450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China

ISBN: (纸本)9781785610899

With the development of the research in data mining, cluster analysis has been widely used in several areas. Aiming at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is proposed on the basis of the existing soft subspace clustering algorithms. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.

关键词： Clustering algorithms

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Generating Multi-modality Virtual Samples with soft DBSCAN for Small data Set Learning

Generating Multi-modality Virtual Samples with Soft DBSCAN f...

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international conference on Applied computing and Information Technology/international conference on Computational science and Intelligence (ACIT-CSI)

作者： Liang-Sian Lin Der-Chiang Li Wei-Hao Yu Yu-Mei Hsueh Department of Industrial and Information Management National Cheng Kung University Tainan Taiwan R.O.C Executive Master of Business Administration Program National Cheng Kung University Tainan Taiwan R.O.C

ISBN: (纸本)9781467396431

Owing to the factors of cost and time limit, the number of samples is usually small in the early stages of manufacturing systems. When the number of available data is small, traditional statistic techniques have difficulty to obtain robust analyses. Therefore, based on a uni-modality distribution assumption, many researchers have proposed virtual sample generation methods to expand the training sample size to enhance the performance of small data set learning. In practice, small data may be following a multi-modality distribution. Therefore, in order to solve multi-modal small data sets, this study proposes a new approach to create multi-modality Weibull virtual samples, where we use the maximal p-value to estimate parameters of the Weibull distribution. In addition, the soft DBSCAN method is used to identify a suitable number of modalities. One data set is employed to check the performance of the proposed method, and comparisons are made by the prediction on root mean square error. The results using a paired t-test show that the proposed method has a superior prediction performance than that of the mega-trend-diffusion method using a uni-modality triangular membership function.

关键词： Clustering algorithms Machine learning algorithms Weibull distribution Sociology Statistics Distributed databases Shape

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Research on stock price forecasting algorithm based on support vector machine

Research on stock price forecasting algorithm based on suppo...

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2015 3rd international conference on soft computing in Information Communication Technology(SCICT 2015)

作者： Zhanmin Wang School of Computer Science and Engineering Xi'an University of Technology

Support vector machine is a kind of machine learning method,which can be trained by using the nonlinear mapping algorithm to transform a low dimensional input space into a high dimensional feature space,so that the high dimensional feature space can be used to analyze the nonlinear characteristics of the *** this paper,we use support vector machine to predict the stock price,and we discuss and experiment with how to select the appropriate historical data attributes of stock price as the training *** experimental results show that the correct data attributes can improve the accuracy of stock price forecasting,but inappropriate data attributes can make the prediction accuracy *** the same time,the training focused on the use of too many of the data attributes will increase the prediction time to a certain extent.

关键词： Support vector machine(SVM) Machine learning Training Prediction

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From Wearables to soft-Wear: Developing soft User Interfaces by Seamlessly Integrating Interactive Technology into Fashionable Apparel 1

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3rd international conference on Design, User Experience, and Usability (DUXU)

作者： Gilgen, Daniel Frankjaer, T. Raune Trier Univ Appl Sci Dept Design D-54290 Trier Germany

ISBN: (数字)9783319076386

ISBN: (纸本)9783319076386;9783319076379

The development of electronic features for use in apparel has advanced rapidly in recent years, and applications in athletic wear have been particularly successful. However, 'Smart Fashion' has not yet been integrated into everyday garments. In this paper we propose a new approach to the design of interfaces in Smart Fashion, which we refer to as the soft User Interface (SUI). The ways in which e-textiles physically convey information differs greatly from traditional ways in that information is communicated via graphical user interfaces on computers, smartphones or on WearComp devices. As a result of our research, we advocate the use of iconic and indexical signs for Smart Fashion as these are widely accessible and understood. As an extension to this new interface paradigm, we expect that the harvesting of biometric data, including bodily gestures, will significantly extend the possibilities of SUIs.

关键词： Smart Fashion applied semantics gestural input embedded electronics physical computing wearable networks hybrid space interactive technology soft User Interface SUI

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Performance of poled organic polymeric films by ANOVA in terms of SHG conversion efficiency 3

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3rd international conference on soft computing for Problem Solving, SocProS 2013

作者： Tyagi, Renu Negi, Yuvraj Singh Pant, Millie Department of Polymer Science and Process Engineering IIT Roorkee Saharanpur Campus Roorkee India Department of Applied Science and Engineering IIT Roorkee Saharanpur Campus Roorkee India

ISBN: (纸本)9788132217671

In this work we have studied the influence of applied DC electric field on the SHG conversion efficiency of poled organic polymeric films. Regression analysis is carried out to get the desired relationship between the input and output parameters. Statistical testing of the models are performed with F-test to obtain the mathematical relationship between input and output parameters. To examine the goodness of fit of a model, the test for significance of regression model is performed and ANOVA is applied to the response data. Curve fitting analysis has been done to perform the accuracy of observed data. © Springer India 2014.

关键词： Curve fitting

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