International Journal of Data Warehousing and Mining最新文献_第10页

A Novel Method for Classifying Function of Spatial Regions Based on Two Sets of Characteristics Indicated by Trajectories 基于轨迹表示的两组特征的空间区域函数分类新方法

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070101

Haitao Zhang, Che Yu, Yan Jin

{"title":"A Novel Method for Classifying Function of Spatial Regions Based on Two Sets of Characteristics Indicated by Trajectories","authors":"Haitao Zhang, Che Yu, Yan Jin","doi":"10.4018/ijdwm.2020070101","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070101","url":null,"abstract":"Trajectoryisasignificantfactorforclassifyingfunctionsofspatialregions.Manyspatialclassification methods use trajectories to detect buildings and districts in urban settings. However, methods thatonly take intoconsideration the localspatiotemporalcharacteristics indicatedby trajectories maygenerateinaccurateresults.Inthisarticle,anovelmethodforclassifyingfunctionofspatial regionsbasedontwosetsofcharacteristicsindicatedbytrajectoriesisproposed,inwhichthelocal spatiotemporalcharacteristicsaswellastheglobalconnectioncharacteristicsareobtainedthrough twosetsofcalculations.Themethodwasevaluatedintwoexperiments:onethatmeasuredchanges in theclassificationmetric throughasplits ratiofactor,andone thatcompared theclassification performancebetweentheproposedmethodandmethodsbasedonasinglesetofcharacteristics.The resultsshowedthattheproposedmethodismoreaccuratethanthetwotraditionalmethods,witha precisionvalueof0.93,arecallvalueof0.77,andanF-Measurevalueof0.84. KeyWoRDS Function of Spatial Regions, Global Connection Characteristics, Local Spatiotemporal Characteristics, Spatial Classification, Trajectory","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"128 1","pages":"1-19"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77056513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem 一种基于增强辅助自适应聚类的欠采样方法处理类失衡问题

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070104

D. Devi, S. Namasudra, Seifedine Kadry

{"title":"A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem","authors":"D. Devi, S. Namasudra, Seifedine Kadry","doi":"10.4018/ijdwm.2020070104","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070104","url":null,"abstract":"The subject of a class imbalance is a well-investigated topic which addresses performance degradation of standard learning models due to uneven distribution of classes in a dataspace. Cluster-based undersampling is a popular solution in the domain which offers to eliminate majority class instances from a definite number of clusters to balance the training data. However, distance-based elimination of instances often got affected by the underlying data distribution. Recently, ensemble learning techniques have emerged as effective solution due to its weighted learning principle of rare instances. In this article, a boosting aided adaptive cluster-based undersampling technique is proposed to facilitate elimination of learning- insignificant majority class instances from the clusters, detected through AdaBoost ensemble learning model. The proposed work is validated with seven existing cluster based undersampling techniques for six binary datasets and three classification models. The experimental results have established the effectives of the proposed technique than the existing methods.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"15 1","pages":"60-86"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81780316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Recommender Systems Using Collaborative Tagging 使用协作标记的推荐系统

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070110

Latha Banda, Karan Singh, Le Hoang Son, Mohamed Abdel-Basset, Pham Huy Thong, H. Huynh, D. Taniar

引用次数: 4

Skeleton Network Extraction and Analysis on Bicycle Sharing Networks 自行车共享网络的骨架网络提取与分析

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070108

Kanokwan Malang, Shuliang Wang, Yuanyuan Lv, Aniwat Phaphuangwittayakul

{"title":"Skeleton Network Extraction and Analysis on Bicycle Sharing Networks","authors":"Kanokwan Malang, Shuliang Wang, Yuanyuan Lv, Aniwat Phaphuangwittayakul","doi":"10.4018/ijdwm.2020070108","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070108","url":null,"abstract":"Skeletonnetworkextractionhasbeenadoptedunevenlyintransportationnetworkswhosenodes are always represented as spatial units. In this article, the TPks skeleton network extraction methodisproposedandappliedtobicyclesharingnetworks.Themethodaimstoreducethe networksizewhilepreservingkeytopologiesandspatialfeatures.Theauthorsquantifiedthe importanceofnodesbyanimprovedtopologypotentialalgorithm.Thespatialclusteringallows todetecthightrafficconcentrationsandallocate thenodesofeachclusteraccordingto their spatialdistribution.Then,theskeletonnetworkisconstructedbyaggregatingthemostimportant indicatedskeletonnodes.Theauthorsexaminetheskeletonnetworkcharacteristicsanddifferent spatialinformationusingtheoriginalnetworksasabenchmark.Theresultsshowthattheskeleton networkscanpreservethetopologicalandspatialinformationsimilartotheoriginalnetworks whilereducingtheirsizeandcomplexity. KEyWoRDS Backbone Extraction, Complex Network, Geographical Information, Network Summarization, Public Bicycle, Spatial Information, Topology Potential, Transportation","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"61 1","pages":"146-167"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84809236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Conceptual Model and Design of Semantic Trajectory Data Warehouse 语义轨迹数据仓库的概念模型与设计

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070106

M. Kwakye

{"title":"Conceptual Model and Design of Semantic Trajectory Data Warehouse","authors":"M. Kwakye","doi":"10.4018/ijdwm.2020070106","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070106","url":null,"abstract":"Thetrajectorypatternsofamovingobjectinaspatio-temporaldomainoffersvariedinformationin termsofthemanagementofthedatageneratedfromthemovement.Thequeryresultsoftrajectory objects from thedatawarehouse areusuallynot enough to answer certain trendbehaviours and meaningfulinferenceswithouttheassociatedsemanticinformationofthetrajectoryobjectorthe geospatialenvironmentwithinaspecifiedpurposeorcontext.Thisarticleformulatesanddesignsa genericontologymodellingframeworkthatservesasthebackgroundmodelplatformforthedesignof asemanticdatawarehousefortrajectories.Themethodologyunderpinsonhighergranularityofdata asaresultofpre-processedandextract-transformed-load(ETL)datasoastoofferefficientsemantic inferencetotheunderlyingtrajectorydata.Moreover,themodellingapproachoutlinesthethematic dimensionsthatofferadesignplatformforpredictivetrendanalysisandknowledgediscoveryinthe trajectorydynamicsanddataprocessingformovingobjects. KeyWoRDS Generic Trajectory Ontology, Multidimensional Entity Relationship, Semantic Annotations, Semantic Trajectory Data Warehouse, Spatio-Temporal Data Modelling","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"13 1","pages":"108-131"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84935765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Serialized Co-Training-Based Recognition of Medicine Names for Patent Mining and Retrieval 基于序列化协同训练的药品名称识别专利挖掘与检索

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070105

Na Deng, Caiquan Xiong

{"title":"Serialized Co-Training-Based Recognition of Medicine Names for Patent Mining and Retrieval","authors":"Na Deng, Caiquan Xiong","doi":"10.4018/ijdwm.2020070105","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070105","url":null,"abstract":"IntheretrievalandminingoftraditionalChinesemedicine(TCM)patents,akeystepisChineseword segmentationandnamedentityrecognition.However,thealiasphenomenonoftraditionalChinese medicinescausesgreatchallengestoChinesewordsegmentationandnamedentityrecognitioninTCM patents,whichdirectlyaffectstheeffectofpatentmining.Becauseofthelackofacomprehensive Chineseherbalmedicinenamethesaurus,traditionalthesaurus-basedChinesewordsegmentation andnamedentityrecognitionarenotsuitableformedicineidentificationinTCMpatents.Inviewof thepresentsituation,usingthelanguagecharacteristicsandstructuralcharacteristicsofTCMpatent texts,amodifiedandserializedco-trainingmethodtorecognizemedicinenamesfromTCMpatent abstract texts isproposed.Experimentsshowthat thismethodcanmaintainhighaccuracyunder relativelylowtimecomplexity.Inaddition,thismethodcanalsobeexpandedtotherecognitionof othernamedentitiesinTCMpatents,suchasdiseasenames,preparationmethods,andsoon. KeyWoRDS Annotation, Co-Training, Machine Learning, Medicine Name, Patent Mining, Patent Retrieval, Traditional Chinese Medicine","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"42 1","pages":"87-107"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73526987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Integrating Feature and Instance Selection Techniques in Opinion Mining 集成特征和实例选择技术的意见挖掘

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070109

Zi-Hung You, Ya-Han Hu, Chih-Fong Tsai, Yen-Ming Kuo

{"title":"Integrating Feature and Instance Selection Techniques in Opinion Mining","authors":"Zi-Hung You, Ya-Han Hu, Chih-Fong Tsai, Yen-Ming Kuo","doi":"10.4018/ijdwm.2020070109","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070109","url":null,"abstract":"Opinion mining focuses on extracting polarity information from texts. For textual term representation,differentfeatureselectionmethods,e.g.termfrequency(TF)ortermfrequency– inverse document frequency (TF–IDF), can yield diverse numbers of text features. In text classification,however,aselectedtrainingsetmaycontainnoisydocuments(oroutliers),which candegrade theclassificationperformance.Tosolve thisproblem, instanceselectioncanbe adoptedtofilteroutunrepresentativetrainingdocuments.Therefore,thisarticleinvestigatesthe opinionminingperformanceassociatedwithfeatureandinstanceselectionstepssimultaneously. Two combination processes based on performing feature selection and instance selection in differentorders,werecompared.Specifically, twofeatureselectionmethods,namelyTFand TF–IDF, and two instance selection methods, namely DROP3 and IB3, were employed for comparison. The experimental results by using three Twitter datasets to develop sentiment classifiersshowedthatTF–IDFfollowedbyDROP3performsthebest. KeyWORDS Feature Selection, Instance Selection, Opinion Mining, Text Classification","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"49 1 1","pages":"168-182"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91046196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Novel Multi-Scale Feature Fusion Method for Region Proposal Network in Fast Object Detection 区域建议网络快速目标检测中一种新的多尺度特征融合方法

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-07-01 DOI: 10.4018/ijdwm.2020070107

Gang Liu, Chuyi Wang

{"title":"A Novel Multi-Scale Feature Fusion Method for Region Proposal Network in Fast Object Detection","authors":"Gang Liu, Chuyi Wang","doi":"10.4018/ijdwm.2020070107","DOIUrl":"https://doi.org/10.4018/ijdwm.2020070107","url":null,"abstract":"Neuralnetworkmodelshavebeenwidelyusedinthefieldofobjectdetecting.Theregionproposal methodsarewidelyusedinthecurrentobjectdetectionnetworksandhaveachievedwellperformance. Thecommonregionproposalmethodshunttheobjectsbygeneratingthousandsofthecandidate boxes.Compared toother regionproposalmethods, the regionproposalnetwork (RPN)method improvestheaccuracyanddetectionspeedwithseveralhundredcandidateboxes.However,sincethe featuremapscontainsinsufficientinformation,theabilityofRPNtodetectandlocatesmall-sized objectsispoor.Anovelmulti-scalefeaturefusionmethodforregionproposalnetworktosolvethe aboveproblemsisproposedinthisarticle.Theproposedmethodiscalledmulti-scaleregionproposal network(MS-RPN)whichcangeneratesuitablefeaturemapsfortheregionproposalnetwork.In MS-RPN,theselectedfeaturemapsatmultiplescalesarefineturnedrespectivelyandcompressed intoauniformspace.Thegeneratedfusionfeaturemapsarecalledrefinedfusionfeatures(RFFs). RFFsincorporateabundantdetailinformationandcontextinformation.AndRFFsaresenttoRPN togeneratebetterregionproposals.TheproposedapproachisevaluatedonPASCALVOC2007 andMSCOCObenchmarktasks.MS-RPNobtainssignificantimprovementsoverthecomparable state-of-the-artdetectionmodels. KeyWORDS Fusion Feature, Multi-Scale, Object Detecting, Region Proposal Network","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"23 1","pages":"132-145"},"PeriodicalIF":1.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89168467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values 程序中的数据挖掘:基于结构度量和执行值的程序聚类

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-04-01 DOI: 10.4018/ijdwm.2020040104

Tiantian Wang, Kechao Wang, Xiaohong Su, Lin Liu

引用次数: 1

Collective Entity Disambiguation Based on Hierarchical Semantic Similarity 基于层次语义相似度的集体实体消歧

IF 1.2 4区计算机科学

International Journal of Data Warehousing and Mining Pub Date : 2020-04-01 DOI: 10.4018/ijdwm.2020040101

Bingjing Jia, Hu Yang, Bin Wu, Ying Xing

引用次数: 2