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international conference on Computer, Communication and Computational Sciences, ICCCCS 2016

作者： Khan, Shifa Dembla, Deepak JECRC University Jaipur India

ISBN: (纸本)9789811037726

Recent concerns regarding privacy breach issues have motivated the development of data mining methods, which preserve the privacy of individual data item. A cluster is gathering of information in such a way that the objects with similar properties are grouped into similar clusters and objects with dissimilar properties are placed into different clusters. The K-Means clustering algorithm is a broadly utilized plan to solve the clustering problem. In this paper, a comparative study of three clustering algorithms—K-means, Hierarchical and Cobweb across two different datasets is being performed. To form Clusters WEKA API has been used. The comparison is made with the variant of standard K-means technique that is Modified K-means technique. The Modified K-means technique has been developed to give better results as compared to existing K-means, Hierarchical and Cobweb techniques. This work also includes encryption and decryption of the formed clusters using AES algorithm to provide privacy to the data while transferring over networks. Experimental result proves that the performance of Modified K-means algorithm is better as compared to the existing K-Means and better than the hierarchical and Cobweb when tested on two datasets. K-Means and Hierarchical clustering is forming less number of clusters. In contrast, Cobweb is forming many clusters, which create memory issues. Therefore, Modified K-means forms an appropriate number of clusters in an organized manner and also takes minimum amount of time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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Design framework for a data mart in the neonatal intensive care unit

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Critical Reviews in Biomedical Engineering 2018年第3期46卷 221-243页

作者： Shirwaikar, Rudresh Deepak Dinesh Acharya, U. Makkithaya, Krishnamoorthi Mallayaswamy, Surulivelrajan Lewis, Leslie Edward Simon Departments of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Pharmacy Practice Manipal Academy of Higher Education Manipal India Department of Paediatrics Manipal Academy of Higher Education Manipal India

Neonates who are critically ill are cared for in a neonatal intensive care unit (NICU) for continuous monitoring of their conditions. Physiological parameters such as heart rate, respiratory wave form, blood oxygen saturation, and body temperature are constantly monitored in the NICU. However, NICUs are not always equipped with a computer system for analyzing such data, identifying critical events, and providing decision support for a neonatologist. Therefore, a specialized computer system, commonly known as a data mart, should be developed for the NICU. An architectural framework for the design and development of an automated system for data collection, storage, and analysis for the NICU is proposed in this paper. Our study also deals with the implementation of advanced transformation functions such as fuzzy grouping and fuzzy lookup for data preparation and preprocessing. Furthermore, based on Kimball’s dimensional modeling, a data gathering and accumulation system, fact constellation schema (galaxy schema) has been built with previously identified neonatal processes. Finally, an information delivery component has been proposed, wherein the neonatal data can be both analyzed with different data mining algorithms and visualized with various metrics. Our pioneering work demonstrates methods that streamline the process of data collection, data storage, analysis, and decision-making, which in turn increases efficiency in the NICU and saves lives. Our study, presented in this paper, describes the design and development process and discusses its utility in the NICU. Our results indicate that data mart is best suited for effective decision-making in the NICU. © 2018 by Begell House, Inc.

关键词： Decision making

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Geographical Relevance Model for Long Tail Point-of-Interest Recommendation 23rd

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23rd international conference on database Systems for advanced Applications (DASFAA).

作者： Liu, Wei Wang, Zhi-Jie Yao, Bin Nie, Mengdie Wang, Jing Mao, Rui Yin, Jian Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Neusoft Inst Guangdong Foshan Peoples R China Shenzhen Univ Shenzhen Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou Peoples R China Guangdong Prov Key Lab Popular High Performance C Shenzhen Peoples R China

ISBN: (纸本)9783319914527;9783319914510

Point-of-Interest (POI) recommendation plays a key role in people's daily life, and has been widely studied in recent years, due to its increasingly applications (e.g., recommending new restaurants for users). One of important phenomena in the POI recommendation community is the data sparsity, which makes deep impact on the quality of recommendation. Existing works have proposed various models to alleviate the bottleneck of the data sparsity, and most of these works addressed this issue from the user perspective. To the best of our knowledge, few attention has been made to address this issue from the POI perspective. In this paper, we observe that the "long tail" POIs, which have few checkins and have less opportunity to be exposed, take up a great proportion among all the POIs. It is interesting and meaningful to investigate the long tail POI recommendation from the POI perspective. To this end, this paper proposes a new model, named GRM (geographical relevance model), that expands POI profiles via relevant POIs and employs the geographical information, addressing the limitations of existing models. Experimental results based on two public datasets demonstrate that our model is effective and competitive. It outperforms state-of-the-art models for the long tail POI recommendation problem.

关键词： Long tail Relevance model Geographical information Point-of-interest recommendation

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Predicting breast cancer risk: A data mining approach 6th

Predicting breast cancer risk: A data mining approach

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6th international conference on the Development of Biomedical Engineering in Vietnam, BME 2016

作者： Tran, Tuan Le, Uyen College of Information and Computer Technology Sullivan University 3101 Bardstown Rd LouisvilleKY40205 United States College of Pharmacy Sullivan University 2100 Gardiner Lane LouisvilleKY40205 United States

ISBN: (纸本)9789811043604

Early prediction of breast cancer plays a critical role in successful treatment and saving lives of thousands of patients every year. Although massive clinical data related to the patients is being collected and stored by healthcare organizations, only a small subset of the predictive factors has been used in predicting outcomes. Most of the existing approaches focus on applying statistical techniques on small set of attributes recommended by the domain-experts disease diagnostics. These conventional approaches usually make unrealistic assumptions, e.g., normality, independence or linearity relationships, which may not be always true in practical data. On the other hand, advanced statistical approaches may address some of the above shortcomings;however, they are computationally expensive and may not applicable to massive datasets. In this study, we use a data mining approach which offers significant advantages over conventional techniques to address the existing limitations. Our data-driven approach can efficiently process clinical dataset to discover patterns and reveal hidden information for early detection and successfully treatment of breast cancer patients. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data mining

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Scientific collaboration networks of the academic Brazilian community of HCI 2018

Scientific collaboration networks of the academic Brazilian ...

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17th Brazilian Symposium on Human Factors in computing Systems, IHC 2018

作者： Ciacia de Mendonça, Felipe Silveira, Milene Selbach Gasparini, Isabela Diniz Junqueira Barbosa, Simone Schroeder, Rebeca PPGCA UDESC Joinville SC Brazil Escola Politécnica PUCRS Porto Alegre RS Brazil PPGCA Depto de Ciência da Computação UDESC Joinville SC Brazil Depto de Informática PUC-Rio Rio de Janeiro RJ Brazil Depto de Ciência da Computação UDESC Joinville SC Brazil

ISBN: (纸本)9781450366014

Social networks enable the creation of specific groups and communities such as scientific collaboration networks, which are composed of their authors, who in turn are connected through their publications. is work generated the networks of scientific collaboration of the Brazilian Community of Human-Computer Interaction analyzing the most prolific authors of the Brazilian Symposia on Human Factors in computing Systems (IHC). First an extraction tool was developed in order to collect data from Laes Platform. Aer the process of preparation, data modeling based on classification techniques, cleaning and standardization of data mining techniques were used. Finally, scientific collaboration networks were generated and analyzed through the bibliometric analysis and social network analysis techniques. e results show IHC community network in relation to its paerns and relationships. ese data can help researchers get insights and increase collaboration among their peers. © 2018 Association for computing Machinery.

关键词： data mining

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A distributed management system for massive GIS data using HBase 4

A distributed management system for massive GIS data using H...

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4th international conference on Fuzzy Systems and data mining, FSDM 2018

作者： Liu, Dawei Li, Xuemei Wang, Haiyang Chinese Academy of Sciences Yantai China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781614999270

data organization, storage, management, analysis and mining are the core of wisdom in smart city. With the development of big data technology and the promotion of smart city construction, how to manage more massive geographic information data has become a hot topic of research. This paper designs a GIS data management system based on distributed database HBase. The system optimizes the generation and storage process of raster data and writes massive raster data to HBase storage and index directly. At the same time, in view of the storage, index and retrieval of vector spatial data, a new design of rowkey is proposed. Considering both the latitude and longitude, and the type and attribute of the spatial data, it can quickly locate the data to be returned through the rowkey of HBase when the vector geographic information is retrieved in space position, and the data will be carried out. Fast filtering can achieve the purpose of retrieving vector spatial data at high speed. The above methods are verified with real GIS data in the cluster environment of HBase. The results show that the proposed system has high data storage and retrieval performance and achieves high efficiency when perform storage and real-time high-speed retrieval of massive geographic information data. © 2018 The authors and IOS Press. All rights reserved.

关键词： Big data

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The Discovery of Micro-blog Users' Interest Groups Based on SSLOK-means Clustering Focus on Big data Improved by Calinski-Harabasz 2

The Discovery of Micro-blog Users' Interest Groups Based on ...

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2nd international conference on data Science and Business Analytics (ICDSBA)

作者： Shen, Yan Tang, Yue Dou, Qian Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang Jiangsu Peoples R China Beijing Foreign Studies Univ Dept Int Business Beijing Peoples R China

ISBN: (纸本)9781538684313

As a data mining algorithm, the clustering has been widely used in the discovery of micro-blog users' interest groups and provides a new idea to mine users' information for the micro-blog operators. The SSLOK-means algorithm newly proposed can solve the drawback of K-means algorithm when clustering the big data. However, the number of clusters still needs to be set manually in advance, which brings difficulties for using. In this paper, an algorithm of semi supervised labels one scan K-means improved by Calinski-Harabasz function is presented and applied to subdivide users and to discover the interest groups. The micro-blog users' data of Sina have been grabbed to do clustering analysis by the new algorithm. Finally, six different types of interest groups including knowledgeable type, material type, IT type, business type, interconnected type and balanced type have been found. The targeted individual recommendations were given for each type of interest group. The research can provide a support for the optimization of personalized service about micro-blog platform and the earnings promotion of micro-blog marketing, which has important theoretical significance and practical value.

关键词： Interest groups Micro-blog Semi-supervised clustering Calinski-Harabasz function SSLOK-means clustering algorithm

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Validation Guideline for Small Scale dataset Classification Result in Medical Domain 17th

Validation Guideline for Small Scale Dataset Classification ...

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17th international conference on Hybrid Intelligent Systems (HIS)

作者： Hwe, Ee Kim Yusoh, Zeratul Izzah Mohd Univ Tekn Malaysia Melaka Ctr Adv Comp Technol Computat Intelligence & Technol Lab Durian Tunggal Malaysia

ISBN: (纸本)9783319763514;9783319763507

datasets are very important information for analysis in any field, but the resources and availability of datasets varies widely. In the medical domain, the difference in the size of datasets can vary from millions to the tens of data. The importance is emphasised on the analysis of large scale datasets because it can provide a large overview of the situation. However, there are some cases where there are only small datasets available, due to some constraints such as high cost of collecting the data and the long hours needed to gather data. This research is to provide a clustering validation guideline on the small scale dataset for future users to verify the usability in the clustering result of small scale dataset. The domain focus is fetal cardiotocography and the small scale dataset with 4 different size will be used. These four datasets will be compared with a large scale dataset that has 2126 data. K-Means is chosen as the clustering technique as it is widely used especially in medical field. Six validation indexes are selected to validate the clustering K-Means technique for all datasets. The result will be obtained and tested in Anderson-Darling test in order to get the normality test result. The guidelines continues with a choice of statistical test either the non-parametric statistical test, Wilcoxon Signed Rank test or the parametric statistical test, Paired-Sample T-Test. Lastly, statistical test result will also be verified with a threshold value to determine the validity of a small scale dataset.

关键词： Validation Small dataset Cluster data mining

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Updating the Discovered High Average-Utility Patterns with Transaction Insertion 11th

Updating the Discovered High Average-Utility Patterns with T...

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11th international conference on Genetic and Evolutionary computing (ICGEC)

作者： Wu, Tsu-Yang Lin, Jerry Chun-Wei Shao, Yinan Fournier-Viger, Philippe Hong, Tzung-Pei Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Fujian Univ Technol Natl Demonstrat Ctr Expt Elect Informat & Elect T Fuzhou Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Grad Sch Shenzhen Peoples R China Harbin Inst Technol Sch Nat Sci & Humanities Shenzhen Grad Sch Shenzhen Peoples R China Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan

ISBN: (纸本)9789811064876;9789811064869

In this paper, we propose an algorithm to handle the transaction insertion for efficiently updating the discovered high average-utility upper-bound itemsets (HAUUBIs) based on the average-utility (AU)-list structure and the Fast UPdated (FUP) concept. The proposed algorithm divides the HAUUBIs existing in the original database and new transactions into four cases, and each case can be respectively maintained to identify the actual high average-utility itemsets (HAUIs) without multiple database scans and enormous candidate generation. Experiments showed that the proposed algorithm has better performance compared to state-of-the-art algorithm in terms of runtime and generates the similar number of candidates.

关键词： Average-utility list HAUIM Incremental Dynamic database Insertion

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SmartDetect: A smart detection scheme for malicious web shell codes via ensemble learning 3rd

SmartDetect: A smart detection scheme for malicious web shel...

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3rd international conference on Smart computing and Communications, SmartCom 2018

作者： Zhang, Zijian Li, Meng Zhu, Liehuang Li, Xinyi School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9783030057541

The rapid global spread of the web technology has led to an increase in unauthorized intrusions into computers and networks. Malicious web shell codes used by hackers can often cause extremely harmful consequences. However, the existing detection methods cannot precisely distinguish between the bad codes and the good codes. To solve this problem, we first detected the malicious web shell codes by applying the traditional data mining algorithms: Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Decision Tree, and Convolutional Neural Network. Then, we designed an ensemble learning classifier to further improve the accuracy. Our experimental analysis proved that the accuracy of SmartDetect—our proposed smart detection scheme for malicious web shell codes—was higher than the accuracy of Shell Detector and NeoPI on the dataset collected from Github. Also, the equal-error rate of the detection result of SmartDetect was lower than those of Shell Detector and NeoPI. © 2018, Springer Nature Switzerland AG.

关键词： data mining

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