PPT-Clustering, Dimensionality Reduction and Instance Based Learning

Clustering Dimensionality Reduction and Instance Based Learning Geoff Hulten Supervised vs Unsupervised Supervised Training samples contain labels Goal learn All algorithms weve explored Logistic regression. 1. Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications. Clustering is an important type of unsupervised learning. PCA was another type of unsupervised learning. Dimensionality Reduction. Author: . Christoph. . Eick. The material is mostly based on the . Shlens. PCA. Tutorial . http://www2.cs.uh.edu/~. ceick/ML/pca.pdf. . and . to a lesser extend based on material . Kenneth D. Harris 24/6/15. Exploratory vs. confirmatory analysis. Exploratory analysis. Helps you formulate a hypothesis. End result is usually a nice-looking picture. Any method is equally valid – because it just helps you think of a hypothesis. Computer Graphics Course. June 2013. What is high dimensional data?. Images. Videos. Documents. Most data, actually!. What is high dimensional data?. Images – dimension 3·X·Y. Videos – dimension of image * number of frames. 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. . 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 . Principle Component Analysis. Why Dimensionality Reduction?. It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increases--------D. L. . Donoho. Curse of dimensionality. k. Ramachandra . murthy. Why Dimensionality Reduction. ?. It . is so easy and convenient to collect . data. Data is not collected only for data mining. Data . accumulates in an unprecedented speed. Data pre-processing . John A. Lee, Michel Verleysen, . Chapter4 . 1. Distance Preservation. دانشگاه صنعتي اميرکبير. (. پلي تکنيک تهران). 2. The motivation behind distance preservation is that any . 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 . Clustering, Dimensionality Reduction and Instance Based Learning Geoff Hulten Supervised vs Unsupervised Supervised Training samples contain labels Goal: learn All algorithms we’ve explored: Logistic regression 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. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. 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 .