PPT-On Simultaneous Clustering and Cleaning over Dirty Data

Shaoxu Song Chunping Li Xiaoquan Zhang Tsinghua University Turn Waste into Wealth Motivation Dirty data commonly exist Often a very large portion Eg GPS readings. Outline. Validating clustering results. 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?. and Physical Interaction . Datasets. Manikandan Narayanan, Adrian Vetta, Eric E. Schadt, Jun Zhu. PLoS Computational Biology 2010. Presented by: Tal Saiag. Seminar in Algorithmic Challenges in Analyzing Big Data* in Biology and . April 22, 2010. Last Time. GMM Model Adaptation. MAP (Maximum A Posteriori). MLLR (Maximum Likelihood Linear Regression). UMB-. MAP. for speaker recognition. Today. Graph Based Clustering. Minimum Cut. Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . algorithm and its application in single . cell . RNA-. seq. data analysis and identification of gain or loss of functions of somatic mutations . Chi Zhang, Ph.D.. Center for Computational Biology and Bioinformatics. 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 . 1. Xiaoming Gao, Emilio Ferrara, Judy . Qiu. School of Informatics and Computing. Indiana University. Outline. Background and motivation. Sequential social media stream clustering algorithm. Parallel algorithm. Suresh Merugu, IITR. Overview. Definition of Clustering. Existing Clustering Methods. Clustering Examples. Classification. Classification Examples. Cluster. : A collection of data objects. Similar to one another within the same cluster. Contents. Motivation. Data. Dimension. ality. . Reduction-MDS, Isomap. Clustering-Kmeans, Ncut, Ratio Cut, SCC. Conclustion. Reference. Motivation. Clustering is a main task of exploratory data mining. 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 . AMB Review 11/2010. Consensus Clustering . (. Monti. et al. 2002). Internal validation method for clustering algorithms.. Stability based technique.. Can be used to compare algorithms or for estimating the number of clusters in the data.. 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 . 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 . Ovens in the average household go through a lot, and while they often help us cook delicious foods, they also get pretty dirty.
Cleaning an oven that has gotten a lot of use over the months and years, can be a time consuming job, particularly when there is stubborn stuck-on food debris to contend with, and all manner of crusty spillages to try and eradicate. In fact, even if you clean your oven every month, depending on the use it gets, this can still be a challenge.