ClusteringGeorg GerberLecture 6 2602Lecture OverviewMotivation – why do clustering Examples from research papersChoosing (dis)similarity measures – a critical step in clusteringEuclidean distancePe
Click to edit Master title styleClick to edit Master text stylesSecond levelThird levelFourth levelFifth levelJian Pei: Data Mining -- Cluster AnalysiP 290-090 Data Mining: Concepts Algorithms and
Klicka h?r f?r att ?ndra formatKlicka h?r f?r att ?ndra format p? bakgrundstextenNiv? tv?Niv? treNiv? fyraNiv? femClusteringPetter MostadClustering vs. class predictionClass prediction: A learning set
ClusteringInstructor: Qiang YangHong Kong University of Science and : . Han I. Witten E. Frank1EssentialsTerminology:Objects = rows = recordsVariables = attributes = featuresA good clustering method
Click to edit Master title styleClick to edit Master text stylesSecond levelThird levelFourth levelFifth levelClustering22604Homework 2 due todayMidterm date: 31104 Project part B assignedIdea and App
ClusteringQiang YangAdapted from Tan et al. and Han et MeasuresTan et Chapter 22Similarity and DissimilaritySimilarityNumerical measure of how alike two data objects higher when objects are more
882014J. Leskovec A. Rajaraman J. Ullman: Mining of Massive Datasets ??ClusteringMining of Massive DatasetsJure Leskovec Anand Rajaraman Jeff Ullman Stanford University Note to other teachers and us
More ClusteringCURE AlgorithmNon-Euclidean Approaches1The CURE AlgorithmProblem with BFRk -means:Assumes clusters are normally distributed in each axes are fixed --- ellipses at an angle are not :A
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