recommendations37
yw372:Com 时间:2021-02-13 阅读:(
)
DISCOVERYANDANALYSISOFWEBUSAGEMININGMARATHEDAGADUMITHARAMR.
C.
PatelA.
C.
S.
College,Shirpur,Maharashtra,IndiaABSTRACTInthispaperwedescribesomeofthemostcommontypesofpatterndiscoveryandanalysistechniquesemployedintheWebusagemining.
InthispapermentionAssociationandClusterAnalysis.
AssociationRuleisafundamentalofDataminingtask.
Itsobjectivetofindallco-occurrencerelationshipcalled,Associationamongdataitem.
LetI={i1,i2,…,im}beasetofitems.
LetT=(t1,t2,…,tn)beasetoftransactions.
ClusteranalysisandvisitorssegmentationClusteringisadataminingtechniquethatgroupstogetherasetofitemshavingsimilarcharacteristics.
Intheusagedomain,therearetwokindsofinterestingclustersthatcanbediscovered:userclustersandpageclusters.
GoalDiscoveryandanalysisofwebusagepatternsusingAssociationanalysis.
DiscoveryandanalysisofwebusagepatternsusingClusterAnalysisandVisitorssegmentation.
KEYWORDS:AssociationAnalysis,ClusterAnalysisandVisitorsSegmentationINTRODUCTIONAssociationrulediscoveryandstatisticalcorrelationanalysiscanfindgroupsofitemsorpagesthatarecommonlyaccessedorpurchasedtogether.
AssociationbasedonApriorialgorithm.
Thisalgorithmfindsgroupsofitemusingsupportandconfidence.
Satisfyingauserspecifiedminimumsupportthreshold.
Suchgroupsofitemsarereferredtoasfrequentitemsets&frequentitemsetsgraph.
Logfilesgeneratedbywebserverscontainenormousamountsofwebusagedatathatispotentiallyvaluableforunderstandingthebehaviorofwebsitevisitors.
Clusteringofuserrecords(sessionsortransactions)isoneofthemostcommonlyusedanalysistasksinWebusageminingandWebanalytics.
Clusteringofuserstendstoestablishgroupsofusersexhibitingsimilarbrowsingpatterns.
Suchknowledgeisespeciallyusefulforinferringuserdemographicsinordertoperformmarketsegmentationine-commerceapplicationsorprovidepersonalizedWebcontenttotheuserswithsimilarinterests.
Furtheranalysisofusergroupsbasedontheirdemographicattributes(e.
g.
,age,gender,incomelevel,etc.
)mayleadtothediscoveryofvaluablebusinessintelligence.
Usage-basedclusteringhasalsobeenusedtocreateWeb-based"usercommunities"reflectingsimilarinterestsofgroupsofusers,andtolearnusermodelsthatcanbeusedtoprovidedynamicrecommendationsinWebpersonalizationapplications.
ASSOCIATIONRULESupport&ConfidenceTheSupportofrule,XYthepercentageoftransactioninTthatcontainsXUY.
nisthenumberoftransactioninT.
Supportisusefulmeasurementofitemsetoritems.
IfXistruethenchecksforY,ifXisfalsethennothingtobesayY.
InthefollowingexampleXunionYthencount.
InternationalJournalofComputerScienceEngineeringandInformationTechnologyResearch(IJCSEITR)ISSN2249-6831Vol.
3,Issue1,Mar2013,313-320TJPRCPvt.
Ltd.
314MaratheDagaduMitharame.
g.
(XUY).
CountSupportN(XUY).
CountConfidenceX.
CountUsingaboveexampleswecanaccepttheminsubandminconf.
Tocalculateminsubandminconfasfollows.
T1C++,JAVA,RUBYT2C++,ASPT3ASP,VBT4C++,JAVA,ASPT5C++,JAVA,PHP,ASP,RUBYT6JAVA,PHP,RUBYT7JAVA,RUBY,PHPJAVA,PHPRUBY[sup=3/7,conf=3/3]Inabove7transactionsJAVA,PHP&RUBYshow3/7times.
EveryitemchecksitemsettoeveryusingJoiningandPruningsteps.
Inwebusageminingsuchrulecanbeusetooptimizestructureofwebsite.
e.
g.
Language,/product/softwareRCPACSCOLLEGEWebsiteEXPERIMENT-FINDINGWEBUSAGEASSOCIATIONRULESInstances:14Attributes:5outlooktemperatureDiscoveryandAnalysisofWebUsageMining315humiditywindyplayIfchecksunny,falseyes[sub1/14conf1/1]Thepurposeofthisexperimentwastogivesomeinsightintotheusefulnessofassociationruleswhentheyareappliedtotheweblogdatasetofaneducationinstitutionandothers.
Weexpectedtofindrulesthatcorrelatetowebpagesthatcontaininformationaboutsunny,rainyortemperatureetc.
SupposethisistransactiontableandfindoutFrequentItemsetthen,T1C++,JAVA,RUBYT2C++,ASPT3ASP,VBT4C++,JAVA,ASPT5C++,JAVA,PHP,ASP,RUBYT6JAVA,PHP,RUBYT7JAVA,RUBY,PHPSize1Size2Size3Size4ItemSetSupp.
ItemSetSupp.
ItemSetSupp.
ItemSetSupp.
C++4C++,JAVA3C++,JAVA,RUBY2C++,JAVA,RUBY,ASP1JAVA5C++,RUBY2C++,JAVA,ASP2C++,JAVA,RUBY,PHP1RUBY4C++,ASP3JAVA,RUBY,ASP1ASP4C++,PHP1JAVA,RUBY,PHP3VB1JAVA,RUBY4RUBY,ASP,PHP1PHP3JAVA,ASP2JAVA,PHP3RUBY,ASP1RUBY,PHP3ASP,PHP1Figure1:WebTransactionsandResultingFrequentItemsets(Minsup=1)FindoutFrequentItemsetbyUsingJoiningandPruningMethodsofAssociationRuleFREQUENTITEMSETGRAPHFig.
2,findsitemsC++andRUBYascandidaterecommendations.
TherecommendationscoresofitemAandCare1,correspondingtotheconfidencesoftherules,JAVA,ASP->C++andJAVA,ASP->RUBY,respectively.
Aproblemwithusingasingleglobalminimumsupportthresholdinassociationruleminingisthatthediscoveredpatternswillnotinclude"rare"butimportantitemswhichmaynotoccurfrequentlyinthetransactiondata.
316MaratheDagaduMitharamC=C++J=JAVAA=ASPR=RUBYP=PHPFigure2:FrequentItemsetsCLUSTERANALYSISANDVISITORSSEGMENTATIONConceptandExampleClusteringofuserrecords(sessionsortransactions)isoneofthemostcommonlyusedanalysistasksinWebusageminingandWebanalytics.
Clusteringofuserstendstoestablishgroupsofusersexhibitingsimilarbrowsingpatterns.
Suchknowledgeisespeciallyusefulforinferringuserdemographicsinordertoperformmarketsegmentationine-commerceapplicationsorprovidepersonalizedWebcontenttotheuserswithsimilarinterests.
DiscoveryandAnalysisofWebUsageMining317HereweUsetheformulaof"WebDataMining"-Bingliubook.
Asanexample,considerthetransactiondatadepictedinsimplicityweassumethatfeature(pageview)weightsineachtransactionvectorarebinary(incontrasttoweightsbasedonafunctionofpageviewduration).
Weassumethatthedatahasalreadybeenclusteredusingastandardclusteringalgorithmsuchask-means,resultinginthreeclustersofusertransactions.
Itshowstheaggregateprofilecorrespondingtocluster1.
Asindicatedbythepageviewweights,pageviewsBandFarethemostsignificantpagescharacterizingthecommoninterestsofusersinthissegment.
PageviewC,however,onlyappearsinonetransactionandmightberemovedgivenafilteringthresholdgreaterthan0.
25.
Suchpatternsareusefulforcharacterizinguserorcustomersegments.
Thisexample,forinstance,indicatesthattheresultingusersegmentisclearlyinterestedinitemsBandFandtoalesserdegreeinitemA.
GivenanewuserwhoshowsinterestinitemsAandB,thispatternmaybeusedtoinferthattheusermightbelongtothissegmentand,therefore,wemightrecommenditemFtothatuser.
ExperimentandResultsInthisexperimentwedefinetable"weather"anddefinefields.
318MaratheDagaduMitharamOutputUsingClusterinWeka===Runinformation===Scheme:weka.
clusterers.
HierarchicalClusterer-N2-LSINGLE-P-A"weka.
core.
EuclideanDistance-Rfirst-last"Relation:weatherInstances:13Attributes:5outlooktemperaturehumiditywindyIgnoredplayTestmode:Classestoclustersevaluationontrainingdata===Modelandevaluationontrainingset===Cluster0((((((1.
0:0.
18505,1.
0:0.
18505):0.
05959,1.
0:0.
24464):0.
7557,(1.
0:0.
16832,(1.
0:0.
08235,1.
0:0.
08235):0.
08597):0.
83201):0.
00109,((0.
0:0.
22986,0.
0:0.
22986):0.
77157,0.
0:1.
00142):0):0.
00106,(0.
0:0.
21648,0.
0:0.
21648):0.
78601):0.
00135,1.
0:1.
00384)ClusteredInstances012(92%)11(8%)Classattribute:playClassestoClusters:01<--assignedtocluster71|yes50|noCluster0<--yesCluster1<--NoclassIncorrectlyclusteredinstances:6.
046.
1538%DiscoveryandAnalysisofWebUsageMining319VisualizationsofPatternsCONCLUSIONSUsagepatternsdiscoveredthroughWebusageminingareeffectiveincapturingitem-to-itemanduser-to-userrelationshipsandsimilaritiesatthelevelofusersessions.
Thispaperhasattemptedtoforthepurposeofwebusagemining.
TheproposedmethodsweresuccessfullytestedonthedatasetordatabasesusingassociationruleandclusteranalysismethodusingWekaTool.
Ourexperimentsconfirmedthatoneofthemajorissuesinassociationruleandclusterfindingistheexistenceoftoomanyrulesandgroups,allofwhichsatisfydefinedconstraints.
REFERENCES1.
Webdatamining–BingLiu320MaratheDagaduMitharam2.
PPTforWebusagemining-BingLiu3.
Srivastava,J.
,Cooley,R.
,Deshpande,M.
,Tan,P.
N.
(2000).
WebUsageMining:DiscoveryandApplicationsofUsagePatternsfromWebData.
ACMSIGKDD,Jan2000.
4.
JaideepSrivastavaPaper5.
WCA.
Webcharacterizationterminology&definitions.
6.
http://www.
w3.
org/1999/05/WCA-terms/.
Vigenteal19/11/2005
欧路云新上了美国洛杉矶cera机房的云服务器,具备弹性云特征(可自定义需要的资源配置:E5-2660 V3、内存、硬盘、流量、带宽),直连网络(联通CUVIP线路),KVM虚拟,自带一个IP,支持购买多个IP,10G的DDoS防御。付款方式:PayPal、支付宝、微信、数字货币(BTC USDT LTC ETH)测试IP:23.224.49.126云服务器 全场8折 优惠码:zhujiceping...
Megalayer 商家算是新晋的服务商,商家才开始的时候主要是以香港、美国独立服务器。后来有新增菲律宾机房,包括有VPS云服务器、独立服务器、站群服务器等产品。线路上有CN2优化带宽、全向带宽和国际带宽,这里有看到商家的特价方案有增加至9个,之前是四个的。在这篇文章中,我来整理看看。第一、香港服务器系列这里香港服务器会根据带宽的不同区别。我这里将香港机房的都整理到一个系列里。核心内存硬盘IP带宽...
A2Hosting主机,A2Hosting怎么样?A2Hosting是UK2集团下属公司,成立于2003年的老牌国外主机商,产品包括虚拟主机、VPS和独立服务器等,数据中心提供包括美国、新加坡softlayer和荷兰三个地区机房。A2Hosting在国外是一家非常大非常有名气的终合型主机商,拥有几百万的客户,非常值得信赖,国外主机论坛对它家的虚拟主机评价非常不错,当前,A2Hosting主机庆祝1...
yw372:Com为你推荐
accessdenied网页打开显示Access Denied,怎么解决linux防火墙设置怎样用iptables配置好Linux防火墙?建企业网站想建立一个企业网站dell服务器bios设置戴尔服务器720bios设置硬盘启动美要求解锁iPhone美版iphone6解锁怎么操作?北京大学cuteftp上海市浦东新区人民法院民事判决书(2009)浦民三(知)初字第206号购物车什么叫淘宝购物车科创板首批名单2019年房产税试点城市名单佛山海虹广东海虹药通电子商务有限公司怎么样?
沈阳虚拟主机 短域名 域名转让 vps是什么意思 免费cn域名 directspace 好玩的桌面 线路工具 e蜗牛 免费个人空间 东莞数据中心 免费cdn 个人免费主页 双线机房 免费mysql数据库 华为云盘 网站加速软件 西安服务器托管 重庆电信服务器托管 帽子云排名 更多