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
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
簡介群聚問題(The clustering problem)群聚方法(The clustering method)標準螞蟻分群演算法(Standard Ant Clustering Algorithm)實驗結果結論4階式聚合演算法(hierarchical agglomerative algorithm)由樹狀結構的底部開始聚合一開始我們將每一筆資料視為一個群聚(cluster)假設我們現在擁有n
违法有害信息,请在下方选择原因提交举报