PPT-Weighted Clustering

Margareta Ackerman Work with Shai BenDavid Simina Branzei and David Loker Clustering is one of the most widely used tools for exploratory data analysis Social Sciences Biology. DESCRIPTION The formula for the standard deviation is EQ 221 while the formula for the weighted standard deviation is EQ 222 where w is the weight for the ith observation N is the number of nonzero weights and is the weighted mean of the observation 1 Weighted Least Squares as a Solution to Heteroskedasticity 5 3 Local Linear Regression 10 4 Exercises 15 1 Weighted Least Squares Instead of minimizing the residual sum of squares RSS 1 x 1 we could minimize the weighted sum of squares WSS 946 Clustering. (adapted from) Prof. Alexander . Ihler. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. +. Unsupervised learning. Supervised learning. Predict target value (“y”) given features (“x”). LOTTERIES.  . SEA Webinar Series: . Weighted Lotteries . Implementing Weighted Lotteries. Colorado Department of Education . Gina . Schlieman. , Charter School Program and Grant Manager. Colorado Context. On-Task Behavior in Children . with . Autism Spectrum Disorders. Brittney . Schorr, MOTS. Agenda. Background. Objective. Intervention. Methodological Quality of Studies. Results. Discussion. Implications for OT Practitioners. Margareta Ackerman. Work with . Shai. Ben-David, . Simina. . Branzei. , and David . Loker. . Clustering is one of the most widely used tools for exploratory data analysis.. . Social Sciences. Biology. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Grade Calculation Equations. Blackboard Learn. Grade Center. 17 September 2014. Weighted Total Grade Calculations in . the Blackboard Learn Grade Center. 1. Total . Score . Calculation. s in General. David . Harel. and . Yehuda. . Koren. KDD 2001. Introduction. Advances in database technologies resulted in huge amounts of spatial data. The characteristics of spatial data pose several difficulties for clustering algorithms.. Chen. Reading: [25.1.2, KPM], [Wang et al., 2009], [Yang . & . Chen, 2011] . 2. Outline. Motivation and Background. Internal index. Motivation and general ideas. Variance-based internal indexes. and Cluster Analysis. Dissertation Defense. Nan Li. Committee. : Dr. . Longin. Jan . Latecki. (Advisor). Dr. . Haibin. Ling. Dr. Slobodan . Vucetic. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .